Overview

Dataset statistics

Number of variables40
Number of observations543
Missing cells4065
Missing cells (%)18.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory169.8 KiB
Average record size in memory320.2 B

Variable types

Numeric21
Categorical19

Warnings

author_id has a high cardinality: 487 distinct values High cardinality
description has a high cardinality: 526 distinct values High cardinality
published_date has a high cardinality: 254 distinct values High cardinality
publisher_id has a high cardinality: 231 distinct values High cardinality
lexile_measure has a high cardinality: 59 distinct values High cardinality
genre_1 has a high cardinality: 70 distinct values High cardinality
genre_2 has a high cardinality: 106 distinct values High cardinality
genre_3 has a high cardinality: 121 distinct values High cardinality
genre_4 has a high cardinality: 141 distinct values High cardinality
genre_5 has a high cardinality: 143 distinct values High cardinality
genre_6 has a high cardinality: 152 distinct values High cardinality
genre_7 has a high cardinality: 152 distinct values High cardinality
genre_8 has a high cardinality: 155 distinct values High cardinality
genre_9 has a high cardinality: 149 distinct values High cardinality
description has 10 (1.8%) missing values Missing
bookedition has 492 (90.6%) missing values Missing
pages has 32 (5.9%) missing values Missing
published_date has 44 (8.1%) missing values Missing
publisher_id has 44 (8.1%) missing values Missing
reading_age has 424 (78.1%) missing values Missing
lexile_measure has 458 (84.3%) missing values Missing
grade_level has 448 (82.5%) missing values Missing
weight has 86 (15.8%) missing values Missing
rating_value_1 has 59 (10.9%) missing values Missing
dimension_0 has 87 (16.0%) missing values Missing
dimension_1 has 87 (16.0%) missing values Missing
dimension_2 has 100 (18.4%) missing values Missing
genre_0 has 51 (9.4%) missing values Missing
genre_1 has 63 (11.6%) missing values Missing
genre_2 has 68 (12.5%) missing values Missing
genre_3 has 72 (13.3%) missing values Missing
genre_4 has 83 (15.3%) missing values Missing
genre_5 has 90 (16.6%) missing values Missing
genre_6 has 98 (18.0%) missing values Missing
genre_7 has 101 (18.6%) missing values Missing
genre_8 has 108 (19.9%) missing values Missing
genre_9 has 113 (20.8%) missing values Missing
genre_0_weight has 51 (9.4%) missing values Missing
genre_1_weight has 63 (11.6%) missing values Missing
genre_2_weight has 68 (12.5%) missing values Missing
genre_3_weight has 72 (13.3%) missing values Missing
genre_4_weight has 83 (15.3%) missing values Missing
genre_5_weight has 90 (16.6%) missing values Missing
genre_6_weight has 98 (18.0%) missing values Missing
genre_7_weight has 101 (18.6%) missing values Missing
genre_8_weight has 108 (19.9%) missing values Missing
genre_9_weight has 113 (20.8%) missing values Missing
author_id is uniformly distributed Uniform
description is uniformly distributed Uniform
lexile_measure is uniformly distributed Uniform
df_index has unique values Unique
rating_value_0 has 18 (3.3%) zeros Zeros
rating_count_0 has 18 (3.3%) zeros Zeros
genre_9_weight has 9 (1.7%) zeros Zeros

Reproduction

Analysis started2021-05-11 02:05:54.368319
Analysis finished2021-05-11 02:06:43.434930
Duration49.07 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct543
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1784.029466
Minimum9
Maximum3541
Zeros0
Zeros (%)0.0%
Memory size4.4 KiB
2021-05-11T09:06:43.503994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile196.3
Q1941
median1730
Q32696.5
95-th percentile3364.8
Maximum3541
Range3532
Interquartile range (IQR)1755.5

Descriptive statistics

Standard deviation1022.861432
Coefficient of variation (CV)0.5733433506
Kurtosis-1.184901313
Mean1784.029466
Median Absolute Deviation (MAD)865
Skewness0.04047813245
Sum968728
Variance1046245.508
MonotocityStrictly increasing
2021-05-11T09:06:43.611091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20471
 
0.2%
34951
 
0.2%
13121
 
0.2%
13131
 
0.2%
26881
 
0.2%
2911
 
0.2%
2921
 
0.2%
13171
 
0.2%
2941
 
0.2%
23431
 
0.2%
Other values (533)533
98.2%
ValueCountFrequency (%)
91
0.2%
171
0.2%
291
0.2%
331
0.2%
441
0.2%
ValueCountFrequency (%)
35411
0.2%
35381
0.2%
35291
0.2%
35221
0.2%
35181
0.2%

author_id
Categorical

HIGH CARDINALITY
UNIFORM

Distinct487
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
author2371
 
6
author2373
 
5
author0441
 
4
author0344
 
3
author0701
 
3
Other values (482)
522 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5430
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique445 ?
Unique (%)82.0%

Sample

1st rowauthor0932
2nd rowauthor2279
3rd rowauthor2386
4th rowauthor2769
5th rowauthor0473
ValueCountFrequency (%)
author23716
 
1.1%
author23735
 
0.9%
author04414
 
0.7%
author03443
 
0.6%
author07013
 
0.6%
author20683
 
0.6%
author26723
 
0.6%
author25763
 
0.6%
author23072
 
0.4%
author06672
 
0.4%
Other values (477)509
93.7%
2021-05-11T09:06:43.838297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
author23716
 
1.1%
author23735
 
0.9%
author04414
 
0.7%
author03443
 
0.6%
author07013
 
0.6%
author20683
 
0.6%
author26723
 
0.6%
author25763
 
0.6%
author23072
 
0.4%
author06672
 
0.4%
Other values (477)509
93.7%

Most occurring characters

ValueCountFrequency (%)
a543
10.0%
u543
10.0%
t543
10.0%
h543
10.0%
o543
10.0%
r543
10.0%
0348
 
6.4%
2344
 
6.3%
1343
 
6.3%
4182
 
3.4%
Other values (6)955
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3258
60.0%
Decimal Number2172
40.0%

Most frequent character per category

ValueCountFrequency (%)
0348
16.0%
2344
15.8%
1343
15.8%
4182
8.4%
7173
8.0%
5165
7.6%
3164
7.6%
6159
7.3%
8148
6.8%
9146
6.7%
ValueCountFrequency (%)
a543
16.7%
u543
16.7%
t543
16.7%
h543
16.7%
o543
16.7%
r543
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin3258
60.0%
Common2172
40.0%

Most frequent character per script

ValueCountFrequency (%)
0348
16.0%
2344
15.8%
1343
15.8%
4182
8.4%
7173
8.0%
5165
7.6%
3164
7.6%
6159
7.3%
8148
6.8%
9146
6.7%
ValueCountFrequency (%)
a543
16.7%
u543
16.7%
t543
16.7%
h543
16.7%
o543
16.7%
r543
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII5430
100.0%

Most frequent character per block

ValueCountFrequency (%)
a543
10.0%
u543
10.0%
t543
10.0%
h543
10.0%
o543
10.0%
r543
10.0%
0348
 
6.4%
2344
 
6.3%
1343
 
6.3%
4182
 
3.4%
Other values (6)955
17.6%

description
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct526
Distinct (%)98.7%
Missing10
Missing (%)1.8%
Memory size4.4 KiB
Award-winning author William Landay has written the consummate novel of an embattled family in crisis - a suspenseful, character-driven mystery that is also a spellbinding tale of guilt, betrayal, and the terrifying speed at which our lives can spin out of control.Andy Barber has been an assistant district attorney in his suburban Massachusetts county for more than twenty years. He is respected in his community, tenacious in the courtroom, and happy at home with his wife, Laurie, and son, Jacob. But when a shocking crime shatters their New England town, Andy is blindsided by what happens next: His fourteen-year-old son is charged with the murder of a fellow student.
 
2
The New York Times bestselling Mortal Instruments continues—and so do the thrills and danger for Jace, Clary, and Simon.What price is too high to pay, even for love? When Jace and Clary meet again, Clary is horrified to discover that the demon Lilith’s magic has bound her beloved Jace together with her evil brother Sebastian, and that Jace has become a servant of evil. The Clave is out to destroy Sebastian, but there is no way to harm one boy without destroying the other. As Alec, Magnus, Simon, and Isabelle wheedle and bargain with Seelies, demons, and the merciless Iron Sisters to try to save Jace, Clary plays a dangerous game of her own. The price of losing is not just her own life, but Jace’s soul. She’s willing to do anything for Jace, but can she still trust him? Or is he truly lost?Love. Blood. Betrayal. Revenge. Darkness threatens to claim the Shadowhunters in the harrowing fifth book of the Mortal Instruments series.
 
2
Sydney Sage is an Alchemist, one of a group of humans who dabble in magic and serve to bridge the worlds of humans and vampires. Alchemists protect vampire secrets - and human lives.Sydney would love to go to college, but instead, she's been sent into hiding at a posh boarding school in Palm Springs, California - tasked with protecting Moroi princess Jill Dragomir from assassins who want to throw the Moroi court into civil war. Formerly in disgrace, Sydney is now praised for her loyalty and obedience, and held up as the model of an exemplary Alchemist.But the closer she grows to Jill, Eddie, and especially Adrian, the more she finds herself questioning her age-old Alchemist beliefs, her idea of family, and her sense of what it means to truly belong. Her world becomes even more complicated when magical experiments show Sydney may hold the key to prevent becoming Strigoi - the fiercest vampires, the ones who don't die. But it's her fear of being just that - special, magical, powerful - that scares her more than anything. Equally daunting is her new romance with Braydon, a cute, brainy guy who seems to be her match in every way. Yet, as perfect as he seems, Sydney finds herself being drawn to someone else - someone forbidden to her.When a shocking secret threatens to tear the vampire world apart, Sydney's loyalties are suddenly tested more than ever before. She wonders how she's supposed to strike a balance between the principles and dogmas she's been taught, and what her instincts are now telling her.Should she trust the Alchemists - or her heart?
 
2
Librarian's note: An alternate cover edition can be found hereIN THE YEAR 2044, reality is an ugly place. The only time teenage Wade Watts really feels alive is when he's jacked into the virtual utopia known as the OASIS. Wade's devoted his life to studying the puzzles hidden within this world's digital confines, puzzles that are based on their creator's obsession with the pop culture of decades past and that promise massive power and fortune to whoever can unlock them. But when Wade stumbles upon the first clue, he finds himself beset by players willing to kill to take this ultimate prize. The race is on, and if Wade's going to survive, he'll have to win—and confront the real world he's always been so desperate to escape.
 
2
I wasn't free of my past, not yet.Sydney's blood is special. That's because she's an alchemist - one of a group of humans who dabble in magic and serve to bridge the worlds of humans and vampires. They protect vampire secrets - and human lives. But the last encounter Sydney had with vampires got her in deep trouble with the other alchemists. And now with her allegiances in question, her future is on the line.When Sydney is torn from her bed in the middle of the night, at first she thinks she's still being punished for her complicated alliance with dhampir Rose Hathaway. But what unfolds is far worse. Jill Dragomir - the sister of Moroi Queen Lissa Dragomir - is in mortal danger, and the Moroi must send her into hiding. To avoid a civil war, Sydney is called upon to act as Jill's guardian and protector, posing as her roommate in the unlikeliest of places: a human boarding school in Palm Springs, California. The last thing Sydney wants is to be accused of sympathizing with vampires. And now she has to live with one.The Moroi court believe Jill and Sydney will be safe at Amberwood Prep, but threats, distractions, and forbidden romance lurk both outside - and within - the school grounds. Now that they're in hiding, the drama is only just beginning.
 
2
Other values (521)
523 

Length

Max length3494
Median length1007
Mean length1118.523452
Min length134

Characters and Unicode

Total characters596173
Distinct characters172
Distinct categories17 ?
Distinct scripts2 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique519 ?
Unique (%)97.4%

Sample

1st rowAt long last, New York Times bestselling author Gena Showalter unveils the story of Paris, the darkest and most tormented Lord of the Underworld.Possessed by the demon of Promiscuity, immortal warrior Paris is irresistibly seductive — but his potent allure comes at a terrible price. Every night he must bed someone new, or weaken and die. And the woman he craves above all others is the one woman he'd thought was forever beyond his reach... until now.Newly possessed by the demon of Wrath, Sienna Blackstone is racked by a ruthless need to punish those around her. Yet in Paris's arms, the vulnerable beauty finds soul-searing passion and incredible peace. Until a blood feud between ancient enemies heats up.Will the battle against gods, angels and creatures of the night bind them eternally — or tear them apart?
2nd rowI sold my future to the man who ruined my past.I had a plan:Sign a contract and board a plane to Ibiza. The anonymous deal would salvage the smoldering wreckage of my life.It would not involve billionaire Harrison King. AKA, the reason I need saving in the first place. He’s as beautiful as he is cruel. A British business titan who’s practically royalty and makes a living getting what he wants.The man flies private. Dates supermodels. But the crisp accent and cocky smirk don’t fool me. He’s a gentleman on the outside, a savage beneath. Dangerous, rough and brutal.Because after my attempt to publicly stand up for those who needed it... He destroyed my reputation. Now, he’s come for the rest of me.Except it’s not salvation he’s promising. It’s nights in Ibiza, under his roof, rules, his control. I sold my soul to a man I hate. Now, he owns me.I can’t back out. No matter what kind of punishment he has in store. Harrison King knows my secrets...But kings keep secrets too.BEAUTIFUL ENEMY is an enthralling, explosive romance from USA Today bestselling author Piper Lawson! You’ll want to binge this addictive new story.
3rd rowFrom Rob Thomas, the creator of groundbreaking television series and movie Veronica Mars, comes the first book in a thrilling new mystery series.Ten years after graduating from high school in Neptune, California, Veronica Mars is back in the land of sun, sand, crime, and corruption. She's traded in her law degree for her old private investigating license, struggling to keep Mars Investigations afloat on the scant cash earned by catching cheating spouses until she can score her first big case.Now it's spring break, and college students descend on Neptune, transforming the beaches and boardwalks into a frenzied, week-long rave. When a girl disappears from a party, Veronica is called in to investigate. But this is not a simple missing person's case. The house the girl vanished from belongs to a man with serious criminal ties, and soon Veronica is plunged into a dangerous underworld of drugs and organized crime. And when a major break in the investigation has a shocking connection to Veronica's past, the case hits closer to home than she ever imagined.
4th rowHannis Arc, working on the tapestry of lines linking constellations of elements that constituted the language of Creation recorded on the ancient Cerulean scroll spread out among the clutter on his desk, was not surprised to see the seven etherial forms billow into the room like acrid smoke driven on a breath of bitter breeze. Like an otherworldly collection of spectral shapes seemingly carried on random eddies of air, they wandered in a loose clutch among the still and silent mounted bears and beasts rising up on their stands, the small forest of stone pedestals holding massive books of recorded prophecy, and the evenly spaced display cases of oddities, their glass reflecting the firelight from the massive hearth at the side of the room.Since the seven rarely used doors, the shutters on the windows down on the ground level several stories below stood open as a fearless show of invitation. Though they frequently chose to use windows, they didn’t actually need the windows any more than they needed the doors. They could seep through any opening, any crack, like vapor rising in the early morning from the stretches of stagnant water that lay in dark swaths through the peat barrens.The open shutters were meant to be a declaration for all to see, including the seven, that Hannis Arc feared nothing.#1 New York Times-bestselling author Terry Goodkind returns to the lives of Richard Rahl and Kahlan Amnell—in a compelling tale of a new and sinister threat to their world.In addition to concluding the Sword of Truth series, The Omen Machine also launches the new series of "Richard and Kahlan."
5th rowC.A. Nicholas's magnum opus symphony is about to begin and he's reserved a seat for you. So come on in and I'll lead you to your place of honour as the house lights dim. Yes, your spot is beside the maestro as he teleports you and he through various worlds to befriend diverse souls who'll reveal the beauty of your life through their stories.***C.A. Nicholas's Interlaced Souls Series has ended and he has collected it into one tome on your behalf. "Cycles of the Phoenix" consists of "Sanity's War", "Strange: And Other Accounts from the Taboo War", and "Kaya: Where Have You Gone?"Winner of 𝑳𝒊𝒕𝒑𝒊𝒄𝒌'𝒔 𝑻𝒐𝒑 𝑪𝒉𝒐𝒊𝒄𝒆 𝑩𝒐𝒐𝒌 𝑨𝒘𝒂𝒓𝒅.Winner of 𝑳𝒊𝒕𝒆𝒓𝒂𝒓𝒚 𝑻𝒊𝒕𝒂𝒏'𝒔 𝑺𝒊𝒍𝒗𝒆𝒓 𝑨𝒘𝒂𝒓𝒅."As subsequent stories drastically recontextualize former ones, the multi-genre 𝘊𝘺𝘤𝘭𝘦𝘴 𝘰𝘧 𝘵𝘩𝘦 𝘗𝘩𝘰𝘦𝘯𝘪𝘹 universe ultimately reveals itself to be an urgently life-affirming, humanizing, and self-empathic tragedy, as well as a bittersweet tale."- 𝑻𝒉𝒆 𝑨𝒕𝒍𝒂𝒏𝒕𝒊𝒔 𝑷𝒐𝒔𝒕"Within one thematically brutal and adult oriented tale that's mostly seen through kids' perceptions, there's an original children's story which allegorizes most of the 'real life' narrative."- 𝑻𝒉𝒆 𝑳𝒂𝒌𝒆 𝑷𝒂𝒓𝒌 𝑻𝒊𝒎𝒆𝒔"This book shows that C.A. Nicholas isn't a poetic storyteller. Rather, he is a poet who creates stories; his tales have the bodies of narratives whose souls are poems, cascading with symbolism, rhythm, dreamlike (including nightmarish) sensory descriptions, events which rhyme with one another, and conventionless form."- 𝑻𝒉𝒆 𝑫𝒓𝒚 𝑻𝒐𝒓𝒕𝒐𝒈𝒖𝒔 𝑪𝒐𝒖𝒓𝒓𝒊𝒆𝒓"Though there is some slasher level brutality here, the overwhelming majority of the violence is psychologically graphic within this empathic book."- 𝑻𝒉𝒆 𝑷𝒂𝒉𝒐𝒐𝒌𝒆𝒆 𝑷𝒐𝒔𝒕"The author's style seems to be inspired by C.S. Lewis, Edgar Allen Poe... and Vincent Van Gough."- 𝑻𝒉𝒆 𝑷𝒂𝒔𝒔𝒊𝒐𝒏𝒂𝒕𝒆 𝑷𝒂𝒊𝒏𝒕𝒆𝒓𝒔' 𝑱𝒐𝒖𝒓𝒏𝒂𝒍"𝘊𝘺𝘤𝘭𝘦𝘴 𝘰𝘧 𝘵𝘩𝘦 𝘗𝘩𝘰𝘦𝘯𝘪𝘹 dares to find uplifting messages not only at the peaks of optimism and humour, but in the deepest trenches of raw trauma, grief, and despair."- 𝑻𝒉𝒆 𝑻𝒘𝒆𝒏𝒕𝒚-𝑭𝒐𝒖𝒓 𝑯𝒐𝒖𝒓 𝑵𝒊𝒈𝒉𝒕𝒔 𝑫𝒊𝒔𝒑𝒂𝒕𝒄𝒉"There are plot driven stories and character driven ones yet even the former variety of tales are rooted in the characters' psychology."- 𝑻𝒉𝒆 𝑪𝒓𝒆𝒂𝒕𝒊𝒗𝒊𝒕𝒚 𝑾𝒊𝒕𝒉𝒊𝒏 𝑨𝒍𝒍 𝒐𝒇 𝑼𝒔 𝑶𝒓𝒈𝒂𝒏𝒊𝒛𝒂𝒕𝒊𝒐𝒏
ValueCountFrequency (%)
Award-winning author William Landay has written the consummate novel of an embattled family in crisis - a suspenseful, character-driven mystery that is also a spellbinding tale of guilt, betrayal, and the terrifying speed at which our lives can spin out of control.Andy Barber has been an assistant district attorney in his suburban Massachusetts county for more than twenty years. He is respected in his community, tenacious in the courtroom, and happy at home with his wife, Laurie, and son, Jacob. But when a shocking crime shatters their New England town, Andy is blindsided by what happens next: His fourteen-year-old son is charged with the murder of a fellow student.2
 
0.4%
The New York Times bestselling Mortal Instruments continues—and so do the thrills and danger for Jace, Clary, and Simon.What price is too high to pay, even for love? When Jace and Clary meet again, Clary is horrified to discover that the demon Lilith’s magic has bound her beloved Jace together with her evil brother Sebastian, and that Jace has become a servant of evil. The Clave is out to destroy Sebastian, but there is no way to harm one boy without destroying the other. As Alec, Magnus, Simon, and Isabelle wheedle and bargain with Seelies, demons, and the merciless Iron Sisters to try to save Jace, Clary plays a dangerous game of her own. The price of losing is not just her own life, but Jace’s soul. She’s willing to do anything for Jace, but can she still trust him? Or is he truly lost?Love. Blood. Betrayal. Revenge. Darkness threatens to claim the Shadowhunters in the harrowing fifth book of the Mortal Instruments series.2
 
0.4%
Sydney Sage is an Alchemist, one of a group of humans who dabble in magic and serve to bridge the worlds of humans and vampires. Alchemists protect vampire secrets - and human lives.Sydney would love to go to college, but instead, she's been sent into hiding at a posh boarding school in Palm Springs, California - tasked with protecting Moroi princess Jill Dragomir from assassins who want to throw the Moroi court into civil war. Formerly in disgrace, Sydney is now praised for her loyalty and obedience, and held up as the model of an exemplary Alchemist.But the closer she grows to Jill, Eddie, and especially Adrian, the more she finds herself questioning her age-old Alchemist beliefs, her idea of family, and her sense of what it means to truly belong. Her world becomes even more complicated when magical experiments show Sydney may hold the key to prevent becoming Strigoi - the fiercest vampires, the ones who don't die. But it's her fear of being just that - special, magical, powerful - that scares her more than anything. Equally daunting is her new romance with Braydon, a cute, brainy guy who seems to be her match in every way. Yet, as perfect as he seems, Sydney finds herself being drawn to someone else - someone forbidden to her.When a shocking secret threatens to tear the vampire world apart, Sydney's loyalties are suddenly tested more than ever before. She wonders how she's supposed to strike a balance between the principles and dogmas she's been taught, and what her instincts are now telling her.Should she trust the Alchemists - or her heart?2
 
0.4%
Librarian's note: An alternate cover edition can be found hereIN THE YEAR 2044, reality is an ugly place. The only time teenage Wade Watts really feels alive is when he's jacked into the virtual utopia known as the OASIS. Wade's devoted his life to studying the puzzles hidden within this world's digital confines, puzzles that are based on their creator's obsession with the pop culture of decades past and that promise massive power and fortune to whoever can unlock them. But when Wade stumbles upon the first clue, he finds himself beset by players willing to kill to take this ultimate prize. The race is on, and if Wade's going to survive, he'll have to win—and confront the real world he's always been so desperate to escape.2
 
0.4%
I wasn't free of my past, not yet.Sydney's blood is special. That's because she's an alchemist - one of a group of humans who dabble in magic and serve to bridge the worlds of humans and vampires. They protect vampire secrets - and human lives. But the last encounter Sydney had with vampires got her in deep trouble with the other alchemists. And now with her allegiances in question, her future is on the line.When Sydney is torn from her bed in the middle of the night, at first she thinks she's still being punished for her complicated alliance with dhampir Rose Hathaway. But what unfolds is far worse. Jill Dragomir - the sister of Moroi Queen Lissa Dragomir - is in mortal danger, and the Moroi must send her into hiding. To avoid a civil war, Sydney is called upon to act as Jill's guardian and protector, posing as her roommate in the unlikeliest of places: a human boarding school in Palm Springs, California. The last thing Sydney wants is to be accused of sympathizing with vampires. And now she has to live with one.The Moroi court believe Jill and Sydney will be safe at Amberwood Prep, but threats, distractions, and forbidden romance lurk both outside - and within - the school grounds. Now that they're in hiding, the drama is only just beginning.2
 
0.4%
Sophie Mercer thought she was a witch. That was the whole reason she was sent to Hex Hall, a reform school for delinquent Prodigium (a.k.a. witches, shape-shifters, and faeries). But then she discovered the family secret, and the fact that her hot crush, Archer Cross, is an agent for The Eye, a group bent on wiping Prodigium off the face of the earth.Turns out, Sophie's a demon, one of only two in the world-the other being her father. What's worse, she has powers that threaten the lives of everyone she loves. Which is precisely why Sophie decides she must go to London for the Removal, a dangerous procedure that will either destroy her powers for good-or kill her. But once Sophie arrives, she makes a shocking discovery. Her new housemates? They're demons too. Meaning, someone is raising demons in secret, with creepy plans to use their powers, and probably not for good. Meanwhile, The Eye is set on hunting Sophie down, and they're using Archer to do it. But it's not like she has feelings for him anymore. Does she?2
 
0.4%
A richly inventive novel about a centuries-old vampire, a spellbound witch, and the mysterious manuscript that draws them together. Deep in the stacks of Oxford's Bodleian Library, young scholar Diana Bishop unwittingly calls up a bewitched alchemical manuscript in the course of her research. Descended from an old and distinguished line of witches, Diana wants nothing to do with sorcery; so after a furtive glance and a few notes, she banishes the book to the stacks. But her discovery sets a fantastical underworld stirring, and a horde of daemons, witches, and vampires soon descends upon the library. Diana has stumbled upon a coveted treasure lost for centuries-and she is the only creature who can break its spell. Debut novelist Deborah Harkness has crafted a mesmerizing and addictive read, equal parts history and magic, romance and suspense. Diana is a bold heroine who meets her equal in vampire geneticist Matthew Clairmont, and gradually warms up to him as their alliance deepens into an intimacy that violates age-old taboos. This smart, sophisticated story harks back to the novels of Anne Rice, but it is as contemporary and sensual as the Twilight series-with an extra serving of historical realism.2
 
0.4%
Long live the King... After turning his back on the throne for centuries, Wrath, son of Wrath, finally assumed his father’s mantle—with the help of his beloved mate. But the crown sets heavily on his head. As the war with the Lessening Society rages on, and the threat from the Band of Bastards truly hits home, he is forced to make choices that put everything—and everyone—at risk.Beth Randall thought she knew what she was getting into when she mated the last pure blooded vampire on the planet: An easy ride was not it. But when she decides she wants a child, she’s unprepared for Wrath’s response—or the distance it creates between them.The question is, will true love win out... or tortured legacy take over?1
 
0.2%
Chris Bosh fell in love with basketball at an early age and earned the prestigious “Mr. Basketball” title while still in high school (Lincoln High School) in Dallas, Texas. A McDonald’s All-American, Bosh was selected fourth overall by the Toronto Raptors after one year attending Georgia Tech. By the end of his basketball career, he was an 11-time NBA All-Star, 2-time Champion and the NBA’s first Global Ambassador of Basketball. In March of 2019, Bosh's #1 Jersey was officially retired for the Miami Heat. In addition to his basketball career, in 2010 Team Tomorrow was founded as a community-uplift organization. Bosh regularly speaks to youths about the benefits of reading, coding and leadership. Bosh, his wife Adrienne, and their five children reside in Austin, Texas.1
 
0.2%
The #1 New York Times bestselling author Kelley Armstrong delivers the novel her fans have been clamoring for: Thirteen, the epic finale of the Otherworld series. It’s been more than ten years, a dozen installments, and hundreds of thousands of copies since Kelley Armstrong introduced readers to the all-too-real denizens of the Otherworld: witches, werewolves, necromancers, vampires, and half-demons, among others. And it’s all been leading to Thirteen, the final installment, the novel that brings all of these stories to a stunning conclusion. A war is brewing—the first battle has been waged and Savannah Levine is left standing, albeit battered and bruised. She has rescued her half brother from supernatural medical testing, but he’s fighting to stay alive. The Supernatural Liberation Movement took him hostage, and they have a maniacal plan to expose the supernatural world to the unknowing.Savannah has called upon her inner energy to summon spells with frightening strength, a strength she never knew she had, as she fights to keep her world from shattering. But it’s more than a matter of supernaturals against one another—both heaven and hell have entered the war; hellhounds, genetically modified werewolves, and all forces of good and evil have joined the fray.Uniting Savannah with Adam, Paige, Lucas, Jaime, Hope, and other lost-but-notforgotten characters in one epic battle, Thirteen is a grand, crowd-pleasing closer for Armstrong’s legions of fans.1
 
0.2%
Other values (516)516
95.0%
(Missing)10
 
1.8%
2021-05-11T09:06:44.065503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the5528
 
5.6%
and3645
 
3.7%
of3045
 
3.1%
a2835
 
2.9%
to2630
 
2.7%
in1717
 
1.7%
is1280
 
1.3%
her1222
 
1.2%
that873
 
0.9%
for816
 
0.8%
Other values (16046)75298
76.1%

Most occurring characters

ValueCountFrequency (%)
98406
16.5%
e58359
 
9.8%
t39385
 
6.6%
a38103
 
6.4%
o34829
 
5.8%
n34594
 
5.8%
i33440
 
5.6%
s31539
 
5.3%
r30889
 
5.2%
h24804
 
4.2%
Other values (162)171825
28.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter463166
77.7%
Space Separator98722
 
16.6%
Uppercase Letter15779
 
2.6%
Other Punctuation13851
 
2.3%
Dash Punctuation2288
 
0.4%
Decimal Number989
 
0.2%
Final Punctuation937
 
0.2%
Close Punctuation122
 
< 0.1%
Open Punctuation121
 
< 0.1%
Initial Punctuation109
 
< 0.1%
Other values (7)89
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e58359
12.6%
t39385
 
8.5%
a38103
 
8.2%
o34829
 
7.5%
n34594
 
7.5%
i33440
 
7.2%
s31539
 
6.8%
r30889
 
6.7%
h24804
 
5.4%
l20616
 
4.5%
Other values (64)116608
25.2%
ValueCountFrequency (%)
A1581
 
10.0%
T1454
 
9.2%
S1202
 
7.6%
B1030
 
6.5%
M905
 
5.7%
C859
 
5.4%
I855
 
5.4%
W807
 
5.1%
H724
 
4.6%
P610
 
3.9%
Other values (34)5752
36.5%
ValueCountFrequency (%)
,6691
48.3%
.4813
34.7%
'1037
 
7.5%
"355
 
2.6%
?271
 
2.0%
:263
 
1.9%
;188
 
1.4%
!97
 
0.7%
48
 
0.3%
#29
 
0.2%
Other values (6)59
 
0.4%
ValueCountFrequency (%)
1250
25.3%
0209
21.1%
9131
13.2%
2112
11.3%
862
 
6.3%
554
 
5.5%
353
 
5.4%
742
 
4.2%
642
 
4.2%
434
 
3.4%
ValueCountFrequency (%)
-1771
77.4%
458
 
20.0%
48
 
2.1%
11
 
0.5%
ValueCountFrequency (%)
98406
99.7%
 312
 
0.3%
4
 
< 0.1%
ValueCountFrequency (%)
>7
63.6%
~2
 
18.2%
+2
 
18.2%
ValueCountFrequency (%)
827
88.3%
110
 
11.7%
ValueCountFrequency (%)
(116
95.9%
[5
 
4.1%
ValueCountFrequency (%)
)117
95.9%
]5
 
4.1%
ValueCountFrequency (%)
107
98.2%
2
 
1.8%
ValueCountFrequency (%)
$9
90.0%
1
 
10.0%
ValueCountFrequency (%)
53
94.6%
—3
 
5.4%
ValueCountFrequency (%)
3
75.0%
1
 
25.0%
ValueCountFrequency (%)
­5
83.3%
1
 
16.7%
ValueCountFrequency (%)
½1
100.0%
ValueCountFrequency (%)
ʼ1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin478693
80.3%
Common117480
 
19.7%

Most frequent character per script

ValueCountFrequency (%)
98406
83.8%
,6691
 
5.7%
.4813
 
4.1%
-1771
 
1.5%
'1037
 
0.9%
827
 
0.7%
458
 
0.4%
"355
 
0.3%
 312
 
0.3%
?271
 
0.2%
Other values (95)2539
 
2.2%
ValueCountFrequency (%)
e58359
12.2%
t39385
 
8.2%
a38103
 
8.0%
o34829
 
7.3%
n34594
 
7.2%
i33440
 
7.0%
s31539
 
6.6%
r30889
 
6.5%
h24804
 
5.2%
l20616
 
4.3%
Other values (57)132135
27.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII593925
99.6%
Punctuation1619
 
0.3%
None371
 
0.1%
Math Alphanum252
 
< 0.1%
Specials3
 
< 0.1%
Modifier Letters1
 
< 0.1%
Currency Symbols1
 
< 0.1%
Letterlike Symbols1
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
98406
16.6%
e58359
 
9.8%
t39385
 
6.6%
a38103
 
6.4%
o34829
 
5.9%
n34594
 
5.8%
i33440
 
5.6%
s31539
 
5.3%
r30889
 
5.2%
h24804
 
4.2%
Other values (77)169577
28.6%
ValueCountFrequency (%)
827
51.1%
458
28.3%
110
 
6.8%
107
 
6.6%
48
 
3.0%
48
 
3.0%
11
 
0.7%
4
 
0.2%
3
 
0.2%
2
 
0.1%
ValueCountFrequency (%)
𝒊19
 
7.5%
𝒆19
 
7.5%
𝒕17
 
6.7%
𝒐17
 
6.7%
𝒓17
 
6.7%
𝒂16
 
6.3%
𝒔13
 
5.2%
𝑻12
 
4.8%
𝒉12
 
4.8%
𝒏9
 
3.6%
Other values (41)101
40.1%
ValueCountFrequency (%)
 312
84.1%
é20
 
5.4%
ó5
 
1.3%
­5
 
1.3%
í4
 
1.1%
—3
 
0.8%
á3
 
0.8%
ì3
 
0.8%
è2
 
0.5%
û2
 
0.5%
Other values (9)12
 
3.2%
ValueCountFrequency (%)
3
100.0%
ValueCountFrequency (%)
ʼ1
100.0%
ValueCountFrequency (%)
1
100.0%
ValueCountFrequency (%)
1
100.0%

bookformat
Categorical

Distinct9
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
Hardcover
363 
Paperback
100 
Kindle Edition
59 
Mass Market Paperback
 
16
Comics
 
1
Other values (4)
 
4

Length

Max length21
Median length9
Mean length9.920810313
Min length6

Characters and Unicode

Total characters5387
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.9%

Sample

1st rowMass Market Paperback
2nd rowKindle Edition
3rd rowPaperback
4th rowHardcover
5th rowPaperback
ValueCountFrequency (%)
Hardcover363
66.9%
Paperback100
 
18.4%
Kindle Edition59
 
10.9%
Mass Market Paperback16
 
2.9%
Comics1
 
0.2%
Board Book1
 
0.2%
Trade Paperback1
 
0.2%
Spiral-bound1
 
0.2%
Unknown Binding1
 
0.2%
2021-05-11T09:06:44.278696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-11T09:06:44.346758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
hardcover363
57.0%
paperback117
 
18.4%
edition59
 
9.3%
kindle59
 
9.3%
market16
 
2.5%
mass16
 
2.5%
binding1
 
0.2%
book1
 
0.2%
unknown1
 
0.2%
trade1
 
0.2%
Other values (3)3
 
0.5%

Most occurring characters

ValueCountFrequency (%)
r862
16.0%
a632
11.7%
e556
10.3%
d485
9.0%
c481
8.9%
o428
7.9%
H363
6.7%
v363
6.7%
i181
 
3.4%
k135
 
2.5%
Other values (21)901
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4655
86.4%
Uppercase Letter637
 
11.8%
Space Separator94
 
1.7%
Dash Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
r862
18.5%
a632
13.6%
e556
11.9%
d485
10.4%
c481
10.3%
o428
9.2%
v363
7.8%
i181
 
3.9%
k135
 
2.9%
n124
 
2.7%
Other values (9)408
8.8%
ValueCountFrequency (%)
H363
57.0%
P117
 
18.4%
K59
 
9.3%
E59
 
9.3%
M32
 
5.0%
B3
 
0.5%
U1
 
0.2%
S1
 
0.2%
C1
 
0.2%
T1
 
0.2%
ValueCountFrequency (%)
94
100.0%
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5292
98.2%
Common95
 
1.8%

Most frequent character per script

ValueCountFrequency (%)
r862
16.3%
a632
11.9%
e556
10.5%
d485
9.2%
c481
9.1%
o428
8.1%
H363
6.9%
v363
6.9%
i181
 
3.4%
k135
 
2.6%
Other values (19)806
15.2%
ValueCountFrequency (%)
94
98.9%
-1
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII5387
100.0%

Most frequent character per block

ValueCountFrequency (%)
r862
16.0%
a632
11.7%
e556
10.3%
d485
9.0%
c481
8.9%
o428
7.9%
H363
6.7%
v363
6.7%
i181
 
3.4%
k135
 
2.5%
Other values (21)901
16.7%

bookedition
Categorical

MISSING

Distinct25
Distinct (%)49.0%
Missing492
Missing (%)90.6%
Memory size4.4 KiB
First Edition
15 
1st Edition
1st edition
Trade
First Edition (U.S.)
 
2
Other values (20)
23 

Length

Max length39
Median length13
Mean length12.64705882
Min length2

Characters and Unicode

Total characters645
Distinct characters48
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)33.3%

Sample

1st rowUS
2nd rowFirst Edition (US)
3rd rowFirst Edition
4th row1st edition
5th row1st edition
ValueCountFrequency (%)
First Edition15
 
2.8%
1st Edition5
 
0.9%
1st edition3
 
0.6%
Trade3
 
0.6%
First Edition (U.S.)2
 
0.4%
US2
 
0.4%
Hyperion, First Edition2
 
0.4%
1st2
 
0.4%
Deckle Edge1
 
0.2%
First1
 
0.2%
Other values (15)15
 
2.8%
(Missing)492
90.6%
2021-05-11T09:06:44.609997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
edition33
31.4%
first25
23.8%
1st11
 
10.5%
us3
 
2.9%
trade3
 
2.9%
hyperion2
 
1.9%
print2
 
1.9%
u.s2
 
1.9%
edge1
 
1.0%
large1
 
1.0%
Other values (22)22
21.0%

Most occurring characters

ValueCountFrequency (%)
i103
16.0%
t76
11.8%
55
 
8.5%
o47
 
7.3%
n46
 
7.1%
r41
 
6.4%
s40
 
6.2%
d40
 
6.2%
E29
 
4.5%
e27
 
4.2%
Other values (38)141
21.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter464
71.9%
Uppercase Letter95
 
14.7%
Space Separator55
 
8.5%
Decimal Number11
 
1.7%
Other Punctuation9
 
1.4%
Open Punctuation5
 
0.8%
Close Punctuation5
 
0.8%
Dash Punctuation1
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
i103
22.2%
t76
16.4%
o47
10.1%
n46
9.9%
r41
 
8.8%
s40
 
8.6%
d40
 
8.6%
e27
 
5.8%
a8
 
1.7%
c5
 
1.1%
Other values (12)31
 
6.7%
ValueCountFrequency (%)
E29
30.5%
F25
26.3%
S9
 
9.5%
U6
 
6.3%
P5
 
5.3%
T4
 
4.2%
B3
 
3.2%
C3
 
3.2%
H2
 
2.1%
A2
 
2.1%
Other values (7)7
 
7.4%
ValueCountFrequency (%)
.4
44.4%
,2
22.2%
'2
22.2%
/1
 
11.1%
ValueCountFrequency (%)
55
100.0%
ValueCountFrequency (%)
(5
100.0%
ValueCountFrequency (%)
)5
100.0%
ValueCountFrequency (%)
111
100.0%
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin559
86.7%
Common86
 
13.3%

Most frequent character per script

ValueCountFrequency (%)
i103
18.4%
t76
13.6%
o47
8.4%
n46
8.2%
r41
 
7.3%
s40
 
7.2%
d40
 
7.2%
E29
 
5.2%
e27
 
4.8%
F25
 
4.5%
Other values (29)85
15.2%
ValueCountFrequency (%)
55
64.0%
111
 
12.8%
(5
 
5.8%
)5
 
5.8%
.4
 
4.7%
,2
 
2.3%
'2
 
2.3%
-1
 
1.2%
/1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII645
100.0%

Most frequent character per block

ValueCountFrequency (%)
i103
16.0%
t76
11.8%
55
 
8.5%
o47
 
7.3%
n46
 
7.1%
r41
 
6.4%
s40
 
6.2%
d40
 
6.2%
E29
 
4.5%
e27
 
4.2%
Other values (38)141
21.9%

pages
Real number (ℝ≥0)

MISSING

Distinct260
Distinct (%)50.9%
Missing32
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean334.9921722
Minimum20
Maximum1098
Zeros0
Zeros (%)0.0%
Memory size4.4 KiB
2021-05-11T09:06:44.720097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile64
Q1256
median329
Q3417
95-th percentile571
Maximum1098
Range1078
Interquartile range (IQR)161

Descriptive statistics

Standard deviation152.134628
Coefficient of variation (CV)0.4541438297
Kurtosis2.849113046
Mean334.9921722
Median Absolute Deviation (MAD)82
Skewness0.6940329971
Sum171181
Variance23144.94504
MonotocityNot monotonic
2021-05-11T09:06:44.828195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32016
 
2.9%
27212
 
2.2%
38411
 
2.0%
3049
 
1.7%
3529
 
1.7%
408
 
1.5%
328
 
1.5%
2407
 
1.3%
5127
 
1.3%
2566
 
1.1%
Other values (250)418
77.0%
(Missing)32
 
5.9%
ValueCountFrequency (%)
201
 
0.2%
281
 
0.2%
328
1.5%
408
1.5%
461
 
0.2%
ValueCountFrequency (%)
10981
0.2%
10161
0.2%
10081
0.2%
9481
0.2%
8491
0.2%

published_date
Categorical

HIGH CARDINALITY
MISSING

Distinct254
Distinct (%)50.9%
Missing44
Missing (%)8.1%
Memory size4.4 KiB
May 18, 2021
 
9
April 3, 2012
 
9
May 25, 2021
 
7
January 1, 2013
 
7
March 1, 2011
 
6
Other values (249)
461 

Length

Max length25
Median length14
Mean length14.25250501
Min length11

Characters and Unicode

Total characters7112
Distinct characters45
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique141 ?
Unique (%)28.3%

Sample

1st rowFebruary 28, 2012
2nd rowMarch 25, 2014
3rd rowAugust 16, 2011
4th rowMay 5, 2019
5th rowFebruary 1, 2011
ValueCountFrequency (%)
May 18, 20219
 
1.7%
April 3, 20129
 
1.7%
May 25, 20217
 
1.3%
January 1, 20137
 
1.3%
March 1, 20116
 
1.1%
May 3, 20116
 
1.1%
July 10, 20126
 
1.1%
February 1, 20116
 
1.1%
May 4, 20216
 
1.1%
October 2, 20126
 
1.1%
Other values (244)431
79.4%
(Missing)44
 
8.1%
2021-05-11T09:06:45.094436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2011130
 
8.7%
2012128
 
8.5%
201394
 
6.3%
202176
 
5.1%
may75
 
5.0%
161
 
4.1%
april61
 
4.1%
september52
 
3.5%
201449
 
3.3%
march48
 
3.2%
Other values (49)724
48.3%

Most occurring characters

ValueCountFrequency (%)
999
14.0%
2890
12.5%
1839
11.8%
0546
 
7.7%
,498
 
7.0%
r335
 
4.7%
e312
 
4.4%
u259
 
3.6%
a254
 
3.6%
y202
 
2.8%
Other values (35)1978
27.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2771
39.0%
Lowercase Letter2340
32.9%
Space Separator999
 
14.0%
Uppercase Letter502
 
7.1%
Other Punctuation499
 
7.0%
Close Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
r335
14.3%
e312
13.3%
u259
11.1%
a254
10.9%
y202
8.6%
b139
 
5.9%
t129
 
5.5%
p113
 
4.8%
l102
 
4.4%
n94
 
4.0%
Other values (9)401
17.1%
ValueCountFrequency (%)
J132
26.3%
M123
24.5%
A104
20.7%
S52
 
10.4%
F41
 
8.2%
O32
 
6.4%
D8
 
1.6%
N6
 
1.2%
B1
 
0.2%
Y1
 
0.2%
Other values (2)2
 
0.4%
ValueCountFrequency (%)
2890
32.1%
1839
30.3%
0546
19.7%
3171
 
6.2%
4103
 
3.7%
562
 
2.2%
749
 
1.8%
848
 
1.7%
641
 
1.5%
922
 
0.8%
ValueCountFrequency (%)
,498
99.8%
;1
 
0.2%
ValueCountFrequency (%)
999
100.0%
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4270
60.0%
Latin2842
40.0%

Most frequent character per script

ValueCountFrequency (%)
r335
 
11.8%
e312
 
11.0%
u259
 
9.1%
a254
 
8.9%
y202
 
7.1%
b139
 
4.9%
J132
 
4.6%
t129
 
4.5%
M123
 
4.3%
p113
 
4.0%
Other values (21)844
29.7%
ValueCountFrequency (%)
999
23.4%
2890
20.8%
1839
19.6%
0546
12.8%
,498
11.7%
3171
 
4.0%
4103
 
2.4%
562
 
1.5%
749
 
1.1%
848
 
1.1%
Other values (4)65
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII7112
100.0%

Most frequent character per block

ValueCountFrequency (%)
999
14.0%
2890
12.5%
1839
11.8%
0546
 
7.7%
,498
 
7.0%
r335
 
4.7%
e312
 
4.4%
u259
 
3.6%
a254
 
3.6%
y202
 
2.8%
Other values (35)1978
27.8%

publisher_id
Categorical

HIGH CARDINALITY
MISSING

Distinct231
Distinct (%)46.3%
Missing44
Missing (%)8.1%
Memory size4.4 KiB
publisher037
 
14
publisher138
 
14
publisher155
 
13
publisher032
 
12
publisher289
 
12
Other values (226)
434 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters5988
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique148 ?
Unique (%)29.7%

Sample

1st rowpublisher149
2nd rowpublisher381
3rd rowpublisher099
4th rowpublisher184
5th rowpublisher074
ValueCountFrequency (%)
publisher03714
 
2.6%
publisher13814
 
2.6%
publisher15513
 
2.4%
publisher03212
 
2.2%
publisher28912
 
2.2%
publisher1059
 
1.7%
publisher3789
 
1.7%
publisher0049
 
1.7%
publisher0918
 
1.5%
publisher2038
 
1.5%
Other values (221)391
72.0%
(Missing)44
 
8.1%
2021-05-11T09:06:45.321642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
publisher13814
 
2.8%
publisher03714
 
2.8%
publisher15513
 
2.6%
publisher03212
 
2.4%
publisher28912
 
2.4%
publisher3789
 
1.8%
publisher0049
 
1.8%
publisher1059
 
1.8%
publisher0918
 
1.6%
publisher2038
 
1.6%
Other values (221)391
78.4%

Most occurring characters

ValueCountFrequency (%)
p499
 
8.3%
u499
 
8.3%
b499
 
8.3%
l499
 
8.3%
i499
 
8.3%
s499
 
8.3%
h499
 
8.3%
e499
 
8.3%
r499
 
8.3%
0248
 
4.1%
Other values (9)1249
20.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4491
75.0%
Decimal Number1497
 
25.0%

Most frequent character per category

ValueCountFrequency (%)
0248
16.6%
3228
15.2%
1205
13.7%
2194
13.0%
8117
7.8%
7116
7.7%
9110
7.3%
4103
6.9%
592
 
6.1%
684
 
5.6%
ValueCountFrequency (%)
p499
11.1%
u499
11.1%
b499
11.1%
l499
11.1%
i499
11.1%
s499
11.1%
h499
11.1%
e499
11.1%
r499
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin4491
75.0%
Common1497
 
25.0%

Most frequent character per script

ValueCountFrequency (%)
0248
16.6%
3228
15.2%
1205
13.7%
2194
13.0%
8117
7.8%
7116
7.7%
9110
7.3%
4103
6.9%
592
 
6.1%
684
 
5.6%
ValueCountFrequency (%)
p499
11.1%
u499
11.1%
b499
11.1%
l499
11.1%
i499
11.1%
s499
11.1%
h499
11.1%
e499
11.1%
r499
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII5988
100.0%

Most frequent character per block

ValueCountFrequency (%)
p499
 
8.3%
u499
 
8.3%
b499
 
8.3%
l499
 
8.3%
i499
 
8.3%
s499
 
8.3%
h499
 
8.3%
e499
 
8.3%
r499
 
8.3%
0248
 
4.1%
Other values (9)1249
20.9%

reading_age
Categorical

MISSING

Distinct31
Distinct (%)26.1%
Missing424
Missing (%)78.1%
Memory size4.4 KiB
8 - 12 years
13 
18 years and up
13 
4 - 8 years
11 
14 years and up
11 
13 years and up
Other values (26)
62 

Length

Max length15
Median length13
Mean length13.21008403
Min length8

Characters and Unicode

Total characters1572
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)10.9%

Sample

1st row14 years and up
2nd row14 years and up
3rd row8 - 12 years
4th row8 - 12 years
5th row12 - 17 years
ValueCountFrequency (%)
8 - 12 years13
 
2.4%
18 years and up13
 
2.4%
4 - 8 years11
 
2.0%
14 years and up11
 
2.0%
13 years and up9
 
1.7%
12 - 15 years7
 
1.3%
10 - 14 years7
 
1.3%
13 - 17 years6
 
1.1%
14 - 17 years6
 
1.1%
15 years and up4
 
0.7%
Other values (21)32
 
5.9%
(Missing)424
78.1%
2021-05-11T09:06:45.523826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
years119
25.1%
74
15.6%
and44
 
9.3%
up44
 
9.3%
1231
 
6.5%
827
 
5.7%
1424
 
5.1%
1318
 
3.8%
1817
 
3.6%
1716
 
3.4%
Other values (11)60
12.7%

Most occurring characters

ValueCountFrequency (%)
355
22.6%
a163
10.4%
1139
 
8.8%
y119
 
7.6%
e119
 
7.6%
r119
 
7.6%
s119
 
7.6%
-74
 
4.7%
n44
 
2.8%
d44
 
2.8%
Other values (11)277
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter815
51.8%
Space Separator355
22.6%
Decimal Number328
20.9%
Dash Punctuation74
 
4.7%

Most frequent character per category

ValueCountFrequency (%)
1139
42.4%
844
 
13.4%
437
 
11.3%
233
 
10.1%
323
 
7.0%
718
 
5.5%
516
 
4.9%
012
 
3.7%
65
 
1.5%
91
 
0.3%
ValueCountFrequency (%)
a163
20.0%
y119
14.6%
e119
14.6%
r119
14.6%
s119
14.6%
n44
 
5.4%
d44
 
5.4%
u44
 
5.4%
p44
 
5.4%
ValueCountFrequency (%)
355
100.0%
ValueCountFrequency (%)
-74
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin815
51.8%
Common757
48.2%

Most frequent character per script

ValueCountFrequency (%)
355
46.9%
1139
 
18.4%
-74
 
9.8%
844
 
5.8%
437
 
4.9%
233
 
4.4%
323
 
3.0%
718
 
2.4%
516
 
2.1%
012
 
1.6%
Other values (2)6
 
0.8%
ValueCountFrequency (%)
a163
20.0%
y119
14.6%
e119
14.6%
r119
14.6%
s119
14.6%
n44
 
5.4%
d44
 
5.4%
u44
 
5.4%
p44
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1572
100.0%

Most frequent character per block

ValueCountFrequency (%)
355
22.6%
a163
10.4%
1139
 
8.8%
y119
 
7.6%
e119
 
7.6%
r119
 
7.6%
s119
 
7.6%
-74
 
4.7%
n44
 
2.8%
d44
 
2.8%
Other values (11)277
17.6%

lexile_measure
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct59
Distinct (%)69.4%
Missing458
Missing (%)84.3%
Memory size4.4 KiB
710L
 
5
850L
 
3
HL710L
 
3
770L
 
3
740L
 
3
Other values (54)
68 

Length

Max length6
Median length4
Mean length4.658823529
Min length2

Characters and Unicode

Total characters396
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)47.1%

Sample

1st rowHL740L
2nd row760L
3rd row880L
4th row710L
5th row930
ValueCountFrequency (%)
710L5
 
0.9%
850L3
 
0.6%
HL710L3
 
0.6%
770L3
 
0.6%
740L3
 
0.6%
950L2
 
0.4%
690L2
 
0.4%
990L2
 
0.4%
HL750L2
 
0.4%
HL680L2
 
0.4%
Other values (49)58
 
10.7%
(Missing)458
84.3%
2021-05-11T09:06:45.768047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
710l5
 
5.9%
hl710l3
 
3.5%
740l3
 
3.5%
850l3
 
3.5%
770l3
 
3.5%
hl750l2
 
2.4%
920l2
 
2.4%
hl740l2
 
2.4%
6902
 
2.4%
990l2
 
2.4%
Other values (49)58
68.2%

Most occurring characters

ValueCountFrequency (%)
L95
24.0%
092
23.2%
739
9.8%
923
 
5.8%
621
 
5.3%
H18
 
4.5%
118
 
4.5%
816
 
4.0%
516
 
4.0%
413
 
3.3%
Other values (7)45
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number255
64.4%
Uppercase Letter141
35.6%

Most frequent character per category

ValueCountFrequency (%)
092
36.1%
739
15.3%
923
 
9.0%
621
 
8.2%
118
 
7.1%
816
 
6.3%
516
 
6.3%
413
 
5.1%
312
 
4.7%
25
 
2.0%
ValueCountFrequency (%)
L95
67.4%
H18
 
12.8%
A11
 
7.8%
D11
 
7.8%
N3
 
2.1%
G2
 
1.4%
P1
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common255
64.4%
Latin141
35.6%

Most frequent character per script

ValueCountFrequency (%)
092
36.1%
739
15.3%
923
 
9.0%
621
 
8.2%
118
 
7.1%
816
 
6.3%
516
 
6.3%
413
 
5.1%
312
 
4.7%
25
 
2.0%
ValueCountFrequency (%)
L95
67.4%
H18
 
12.8%
A11
 
7.8%
D11
 
7.8%
N3
 
2.1%
G2
 
1.4%
P1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII396
100.0%

Most frequent character per block

ValueCountFrequency (%)
L95
24.0%
092
23.2%
739
9.8%
923
 
5.8%
621
 
5.3%
H18
 
4.5%
118
 
4.5%
816
 
4.0%
516
 
4.0%
413
 
3.3%
Other values (7)45
11.4%

grade_level
Categorical

MISSING

Distinct24
Distinct (%)25.3%
Missing448
Missing (%)82.5%
Memory size4.4 KiB
7 - 9
16 
3 - 7
13 
9 - 12
12 
Preschool - 3
10 
5 - 9
Other values (19)
37 

Length

Max length24
Median length5
Mean length7.284210526
Min length5

Characters and Unicode

Total characters692
Distinct characters29
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)8.4%

Sample

1st row9 - 12
2nd row3 - 7
3rd row3 - 7
4th row7 - 9
5th row10 - 12
ValueCountFrequency (%)
7 - 916
 
2.9%
3 - 713
 
2.4%
9 - 1212
 
2.2%
Preschool - 310
 
1.8%
5 - 97
 
1.3%
8 and up5
 
0.9%
10 and up3
 
0.6%
8 - 93
 
0.6%
10 - 123
 
0.6%
8 - 123
 
0.6%
Other values (14)20
 
3.7%
(Missing)448
82.5%
2021-05-11T09:06:45.995253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
85
29.8%
941
14.4%
734
 
11.9%
324
 
8.4%
1220
 
7.0%
preschool16
 
5.6%
812
 
4.2%
511
 
3.9%
up10
 
3.5%
and10
 
3.5%
Other values (6)22
 
7.7%

Most occurring characters

ValueCountFrequency (%)
190
27.5%
-85
12.3%
941
 
5.9%
734
 
4.9%
o32
 
4.6%
129
 
4.2%
324
 
3.5%
223
 
3.3%
r22
 
3.2%
e22
 
3.2%
Other values (19)190
27.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter211
30.5%
Space Separator190
27.5%
Decimal Number187
27.0%
Dash Punctuation85
12.3%
Uppercase Letter19
 
2.7%

Most frequent character per category

ValueCountFrequency (%)
o32
15.2%
r22
10.4%
e22
10.4%
n16
7.6%
s16
7.6%
c16
7.6%
h16
7.6%
l16
7.6%
a13
6.2%
d13
6.2%
Other values (5)29
13.7%
ValueCountFrequency (%)
941
21.9%
734
18.2%
129
15.5%
324
12.8%
223
12.3%
812
 
6.4%
511
 
5.9%
06
 
3.2%
65
 
2.7%
42
 
1.1%
ValueCountFrequency (%)
P16
84.2%
K3
 
15.8%
ValueCountFrequency (%)
190
100.0%
ValueCountFrequency (%)
-85
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common462
66.8%
Latin230
33.2%

Most frequent character per script

ValueCountFrequency (%)
o32
13.9%
r22
9.6%
e22
9.6%
n16
 
7.0%
P16
 
7.0%
s16
 
7.0%
c16
 
7.0%
h16
 
7.0%
l16
 
7.0%
a13
 
5.7%
Other values (7)45
19.6%
ValueCountFrequency (%)
190
41.1%
-85
18.4%
941
 
8.9%
734
 
7.4%
129
 
6.3%
324
 
5.2%
223
 
5.0%
812
 
2.6%
511
 
2.4%
06
 
1.3%
Other values (2)7
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII692
100.0%

Most frequent character per block

ValueCountFrequency (%)
190
27.5%
-85
12.3%
941
 
5.9%
734
 
4.9%
o32
 
4.6%
129
 
4.2%
324
 
3.5%
223
 
3.3%
r22
 
3.2%
e22
 
3.2%
Other values (19)190
27.5%

weight
Real number (ℝ≥0)

MISSING

Distinct167
Distinct (%)36.5%
Missing86
Missing (%)15.8%
Infinite0
Infinite (%)0.0%
Mean2306.828293
Minimum453.59
Maximum7212.11
Zeros0
Zeros (%)0.0%
Memory size4.4 KiB
2021-05-11T09:06:46.098347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum453.59
5-th percentile488.066
Q1612.35
median793.79
Q33991.61
95-th percentile6667.8
Maximum7212.11
Range6758.52
Interquartile range (IQR)3379.26

Descriptive statistics

Standard deviation2252.952283
Coefficient of variation (CV)0.9766449846
Kurtosis-0.7922520802
Mean2306.828293
Median Absolute Deviation (MAD)290.3
Skewness0.9066943988
Sum1054220.53
Variance5075793.989
MonotocityNot monotonic
2021-05-11T09:06:46.197437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
612.3516
 
2.9%
566.9915
 
2.8%
680.3914
 
2.6%
453.5913
 
2.4%
5805.9812
 
2.2%
3628.7411
 
2.0%
635.0310
 
1.8%
789.2510
 
1.8%
589.679
 
1.7%
6894.69
 
1.7%
Other values (157)338
62.2%
(Missing)86
 
15.8%
ValueCountFrequency (%)
453.5913
2.4%
462.663
 
0.6%
467.21
 
0.2%
471.742
 
0.4%
476.273
 
0.6%
ValueCountFrequency (%)
7212.112
0.4%
7166.751
 
0.2%
7030.682
0.4%
6985.324
0.7%
6939.961
 
0.2%

rating_value_0
Real number (ℝ≥0)

ZEROS

Distinct121
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.928692449
Minimum0
Maximum5
Zeros18
Zeros (%)3.3%
Memory size4.4 KiB
2021-05-11T09:06:46.307536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.33
Q13.835
median4.04
Q34.25
95-th percentile4.667
Maximum5
Range5
Interquartile range (IQR)0.415

Descriptive statistics

Standard deviation0.8053020469
Coefficient of variation (CV)0.2049796611
Kurtosis16.23864568
Mean3.928692449
Median Absolute Deviation (MAD)0.21
Skewness-3.772004924
Sum2133.28
Variance0.6485113868
MonotocityNot monotonic
2021-05-11T09:06:46.422642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
519
 
3.5%
018
 
3.3%
4.0313
 
2.4%
412
 
2.2%
4.1212
 
2.2%
4.2511
 
2.0%
3.9211
 
2.0%
3.9911
 
2.0%
4.0811
 
2.0%
4.1710
 
1.8%
Other values (111)415
76.4%
ValueCountFrequency (%)
018
3.3%
21
 
0.2%
3.131
 
0.2%
3.181
 
0.2%
3.251
 
0.2%
ValueCountFrequency (%)
519
3.5%
4.911
 
0.2%
4.892
 
0.4%
4.81
 
0.2%
4.771
 
0.2%

rating_value_1
Real number (ℝ≥0)

MISSING

Distinct18
Distinct (%)3.7%
Missing59
Missing (%)10.9%
Infinite0
Infinite (%)0.0%
Mean4.517768595
Minimum3.2
Maximum5
Zeros0
Zeros (%)0.0%
Memory size4.4 KiB
2021-05-11T09:06:46.522732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3.2
5-th percentile4
Q14.4
median4.6
Q34.7
95-th percentile4.9
Maximum5
Range1.8
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.2857817366
Coefficient of variation (CV)0.06325727638
Kurtosis2.141523018
Mean4.517768595
Median Absolute Deviation (MAD)0.2
Skewness-1.108738606
Sum2186.6
Variance0.081671201
MonotocityNot monotonic
2021-05-11T09:06:46.603806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
4.690
16.6%
4.776
14.0%
4.572
13.3%
4.859
10.9%
4.444
8.1%
4.342
7.7%
522
 
4.1%
4.222
 
4.1%
4.118
 
3.3%
4.99
 
1.7%
Other values (8)30
 
5.5%
(Missing)59
10.9%
ValueCountFrequency (%)
3.21
 
0.2%
3.31
 
0.2%
3.41
 
0.2%
3.62
 
0.4%
3.76
1.1%
ValueCountFrequency (%)
522
 
4.1%
4.99
 
1.7%
4.859
10.9%
4.776
14.0%
4.690
16.6%

rating_count_0
Real number (ℝ≥0)

ZEROS

Distinct478
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63565.25414
Minimum0
Maximum3087177
Zeros18
Zeros (%)3.3%
Memory size4.4 KiB
2021-05-11T09:06:46.708901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11637.5
median13453
Q348656
95-th percentile257034.3
Maximum3087177
Range3087177
Interquartile range (IQR)47018.5

Descriptive statistics

Standard deviation194364.5191
Coefficient of variation (CV)3.057716386
Kurtosis117.1691523
Mean63565.25414
Median Absolute Deviation (MAD)13421
Skewness9.115801467
Sum34515933
Variance3.77775663 × 1010
MonotocityNot monotonic
2021-05-11T09:06:46.821002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
018
 
3.3%
114
 
2.6%
28
 
1.5%
36
 
1.1%
55
 
0.9%
103
 
0.6%
302
 
0.4%
322
 
0.4%
11082
 
0.4%
2755442
 
0.4%
Other values (468)481
88.6%
ValueCountFrequency (%)
018
3.3%
114
2.6%
28
1.5%
36
 
1.1%
55
 
0.9%
ValueCountFrequency (%)
30871771
0.2%
12372291
0.2%
12337741
0.2%
11859471
0.2%
10042161
0.2%

rating_count_1
Real number (ℝ≥0)

Distinct423
Distinct (%)77.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2180.384899
Minimum1
Maximum40409
Zeros0
Zeros (%)0.0%
Memory size4.4 KiB
2021-05-11T09:06:46.943113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q1109.5
median580
Q32132.5
95-th percentile10004.3
Maximum40409
Range40408
Interquartile range (IQR)2023

Descriptive statistics

Standard deviation4447.645664
Coefficient of variation (CV)2.03984428
Kurtosis27.46443897
Mean2180.384899
Median Absolute Deviation (MAD)578
Skewness4.542124026
Sum1183949
Variance19781551.95
MonotocityNot monotonic
2021-05-11T09:06:47.053213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
164
 
11.8%
24
 
0.7%
34
 
0.7%
94
 
0.7%
73
 
0.6%
153
 
0.6%
1663
 
0.6%
113
 
0.6%
603
 
0.6%
302
 
0.4%
Other values (413)450
82.9%
ValueCountFrequency (%)
164
11.8%
24
 
0.7%
34
 
0.7%
41
 
0.2%
52
 
0.4%
ValueCountFrequency (%)
404091
0.2%
370592
0.4%
286591
0.2%
207651
0.2%
194011
0.2%

dimension_0
Real number (ℝ≥0)

MISSING

Distinct146
Distinct (%)32.0%
Missing87
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean15.63625
Minimum1.47
Maximum30.48
Zeros0
Zeros (%)0.0%
Memory size4.4 KiB
2021-05-11T09:06:47.169318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.47
5-th percentile10.7775
Q113.97
median15.24
Q316.41
95-th percentile21.59
Maximum30.48
Range29.01
Interquartile range (IQR)2.44

Descriptive statistics

Standard deviation3.376013674
Coefficient of variation (CV)0.2159094203
Kurtosis6.525101689
Mean15.63625
Median Absolute Deviation (MAD)1.205
Skewness0.5116488326
Sum7130.13
Variance11.39746832
MonotocityNot monotonic
2021-05-11T09:06:47.268409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.2456
 
10.3%
13.9744
 
8.1%
16.5129
 
5.3%
15.8820
 
3.7%
16.2611
 
2.0%
16.2110
 
1.8%
15.579
 
1.7%
14.619
 
1.7%
13.499
 
1.7%
167
 
1.3%
Other values (136)252
46.4%
(Missing)87
 
16.0%
ValueCountFrequency (%)
1.471
0.2%
1.931
0.2%
2.542
0.4%
3.171
0.2%
3.431
0.2%
ValueCountFrequency (%)
30.481
0.2%
29.841
0.2%
29.211
0.2%
28.71
0.2%
28.652
0.4%

dimension_1
Real number (ℝ≥0)

MISSING

Distinct147
Distinct (%)32.2%
Missing87
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean3.801973684
Minimum0.23
Maximum27.43
Zeros0
Zeros (%)0.0%
Memory size4.4 KiB
2021-05-11T09:06:47.375506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.23
5-th percentile1.0125
Q12.2325
median2.82
Q33.5875
95-th percentile14.88
Maximum27.43
Range27.2
Interquartile range (IQR)1.355

Descriptive statistics

Standard deviation4.153031628
Coefficient of variation (CV)1.092335711
Kurtosis12.44348873
Mean3.801973684
Median Absolute Deviation (MAD)0.74
Skewness3.549463834
Sum1733.7
Variance17.2476717
MonotocityNot monotonic
2021-05-11T09:06:47.484605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.5437
 
6.8%
3.8120
 
3.7%
3.1716
 
2.9%
2.7916
 
2.9%
3.314
 
2.6%
1.2713
 
2.4%
4.4513
 
2.4%
3.5610
 
1.8%
1.5210
 
1.8%
2.299
 
1.7%
Other values (137)298
54.9%
(Missing)87
 
16.0%
ValueCountFrequency (%)
0.231
 
0.2%
0.462
0.4%
0.511
 
0.2%
0.531
 
0.2%
0.643
0.6%
ValueCountFrequency (%)
27.431
 
0.2%
23.52
0.4%
22.862
0.4%
21.593
0.6%
21.341
 
0.2%

dimension_2
Real number (ℝ≥0)

MISSING

Distinct129
Distinct (%)29.1%
Missing100
Missing (%)18.4%
Infinite0
Infinite (%)0.0%
Mean22.23336343
Minimum1.02
Maximum28.91
Zeros0
Zeros (%)0.0%
Memory size4.4 KiB
2021-05-11T09:06:47.593704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.02
5-th percentile17.288
Q121.08
median22.86
Q324.13
95-th percentile25.4
Maximum28.91
Range27.89
Interquartile range (IQR)3.05

Descriptive statistics

Standard deviation3.230162671
Coefficient of variation (CV)0.1452844812
Kurtosis17.33639079
Mean22.23336343
Median Absolute Deviation (MAD)1.27
Skewness-3.257310655
Sum9849.38
Variance10.43395088
MonotocityNot monotonic
2021-05-11T09:06:47.701802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.8650
 
9.2%
24.1330
 
5.5%
20.9530
 
5.5%
23.529
 
5.3%
21.5921
 
3.9%
20.3214
 
2.6%
21.749
 
1.7%
22.239
 
1.7%
24.218
 
1.5%
24.188
 
1.5%
Other values (119)235
43.3%
(Missing)100
18.4%
ValueCountFrequency (%)
1.021
0.2%
2.541
0.2%
2.91
0.2%
3.051
0.2%
3.171
0.2%
ValueCountFrequency (%)
28.911
 
0.2%
28.731
 
0.2%
28.573
0.6%
27.941
 
0.2%
27.641
 
0.2%

genre_0
Categorical

MISSING

Distinct45
Distinct (%)9.1%
Missing51
Missing (%)9.4%
Memory size4.4 KiB
Nonfiction
67 
Fantasy
61 
Fiction
57 
Historical Fiction
33 
Young Adult
29 
Other values (40)
245 

Length

Max length18
Median length9
Mean length9.666666667
Min length4

Characters and Unicode

Total characters4756
Distinct characters39
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)3.5%

Sample

1st rowParanormal Romance
2nd rowMystery
3rd rowFantasy
4th rowMemoir
5th rowFantasy
ValueCountFrequency (%)
Nonfiction67
12.3%
Fantasy61
11.2%
Fiction57
 
10.5%
Historical Fiction33
 
6.1%
Young Adult29
 
5.3%
Science Fiction22
 
4.1%
Mystery21
 
3.9%
Picture Books19
 
3.5%
Romance18
 
3.3%
Poetry17
 
3.1%
Other values (35)148
27.3%
(Missing)51
 
9.4%
2021-05-11T09:06:47.957033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fiction113
17.4%
fantasy77
 
11.9%
nonfiction67
 
10.3%
historical33
 
5.1%
adult29
 
4.5%
young29
 
4.5%
science23
 
3.5%
romance22
 
3.4%
mystery22
 
3.4%
books19
 
2.9%
Other values (41)215
33.1%

Most occurring characters

ValueCountFrequency (%)
o552
11.6%
i544
 
11.4%
n437
 
9.2%
t402
 
8.5%
c324
 
6.8%
a293
 
6.2%
r235
 
4.9%
s211
 
4.4%
e197
 
4.1%
F196
 
4.1%
Other values (29)1365
28.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3947
83.0%
Uppercase Letter652
 
13.7%
Space Separator157
 
3.3%

Most frequent character per category

ValueCountFrequency (%)
o552
14.0%
i544
13.8%
n437
11.1%
t402
10.2%
c324
8.2%
a293
7.4%
r235
 
6.0%
s211
 
5.3%
e197
 
5.0%
y157
 
4.0%
Other values (11)595
15.1%
ValueCountFrequency (%)
F196
30.1%
N81
12.4%
H59
 
9.0%
P47
 
7.2%
M44
 
6.7%
S32
 
4.9%
A30
 
4.6%
Y29
 
4.4%
C28
 
4.3%
B28
 
4.3%
Other values (7)78
 
12.0%
ValueCountFrequency (%)
157
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4599
96.7%
Common157
 
3.3%

Most frequent character per script

ValueCountFrequency (%)
o552
12.0%
i544
11.8%
n437
 
9.5%
t402
 
8.7%
c324
 
7.0%
a293
 
6.4%
r235
 
5.1%
s211
 
4.6%
e197
 
4.3%
F196
 
4.3%
Other values (28)1208
26.3%
ValueCountFrequency (%)
157
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4756
100.0%

Most frequent character per block

ValueCountFrequency (%)
o552
11.6%
i544
 
11.4%
n437
 
9.2%
t402
 
8.5%
c324
 
6.8%
a293
 
6.2%
r235
 
4.9%
s211
 
4.4%
e197
 
4.1%
F196
 
4.1%
Other values (29)1365
28.7%

genre_1
Categorical

HIGH CARDINALITY
MISSING

Distinct70
Distinct (%)14.6%
Missing63
Missing (%)11.6%
Memory size4.4 KiB
Fiction
84 
Nonfiction
49 
Young Adult
31 
Fantasy
 
25
Historical Fiction
 
23
Other values (65)
268 

Length

Max length20
Median length8
Mean length9.064583333
Min length4

Characters and Unicode

Total characters4351
Distinct characters47
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)4.8%

Sample

1st rowParanormal
2nd rowFiction
3rd rowFiction
4th rowNonfiction
5th rowYoung Adult
ValueCountFrequency (%)
Fiction84
15.5%
Nonfiction49
 
9.0%
Young Adult31
 
5.7%
Fantasy25
 
4.6%
Historical Fiction23
 
4.2%
Romance18
 
3.3%
Memoir18
 
3.3%
Contemporary18
 
3.3%
Childrens14
 
2.6%
Humor14
 
2.6%
Other values (60)186
34.3%
(Missing)63
 
11.6%
2021-05-11T09:06:48.192246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fiction117
19.8%
nonfiction49
 
8.3%
adult36
 
6.1%
fantasy32
 
5.4%
young31
 
5.2%
historical27
 
4.6%
romance25
 
4.2%
contemporary21
 
3.5%
memoir18
 
3.0%
childrens14
 
2.4%
Other values (64)222
37.5%

Most occurring characters

ValueCountFrequency (%)
i535
 
12.3%
o485
 
11.1%
n389
 
8.9%
t337
 
7.7%
c276
 
6.3%
a241
 
5.5%
r235
 
5.4%
e187
 
4.3%
F155
 
3.6%
s154
 
3.5%
Other values (37)1357
31.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3638
83.6%
Uppercase Letter601
 
13.8%
Space Separator112
 
2.6%

Most frequent character per category

ValueCountFrequency (%)
F155
25.8%
N64
10.6%
C62
 
10.3%
H57
 
9.5%
M44
 
7.3%
A38
 
6.3%
Y31
 
5.2%
R31
 
5.2%
S20
 
3.3%
P18
 
3.0%
Other values (14)81
13.5%
ValueCountFrequency (%)
i535
14.7%
o485
13.3%
n389
10.7%
t337
9.3%
c276
7.6%
a241
 
6.6%
r235
 
6.5%
e187
 
5.1%
s154
 
4.2%
l145
 
4.0%
Other values (12)654
18.0%
ValueCountFrequency (%)
112
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4239
97.4%
Common112
 
2.6%

Most frequent character per script

ValueCountFrequency (%)
i535
12.6%
o485
 
11.4%
n389
 
9.2%
t337
 
7.9%
c276
 
6.5%
a241
 
5.7%
r235
 
5.5%
e187
 
4.4%
F155
 
3.7%
s154
 
3.6%
Other values (36)1245
29.4%
ValueCountFrequency (%)
112
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4351
100.0%

Most frequent character per block

ValueCountFrequency (%)
i535
 
12.3%
o485
 
11.1%
n389
 
8.9%
t337
 
7.7%
c276
 
6.3%
a241
 
5.5%
r235
 
5.4%
e187
 
4.3%
F155
 
3.6%
s154
 
3.5%
Other values (37)1357
31.2%

genre_2
Categorical

HIGH CARDINALITY
MISSING

Distinct106
Distinct (%)22.3%
Missing68
Missing (%)12.5%
Memory size4.4 KiB
Fiction
43 
Contemporary
 
28
Romance
 
23
Fantasy
 
22
Humor
 
20
Other values (101)
339 

Length

Max length20
Median length7
Mean length8.606315789
Min length3

Characters and Unicode

Total characters4088
Distinct characters45
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)9.7%

Sample

1st rowRomance
2nd rowAudiobook
3rd rowEpic Fantasy
4th rowHumor
5th rowRomance
ValueCountFrequency (%)
Fiction43
 
7.9%
Contemporary28
 
5.2%
Romance23
 
4.2%
Fantasy22
 
4.1%
Humor20
 
3.7%
Paranormal18
 
3.3%
Mystery16
 
2.9%
Memoir16
 
2.9%
Historical13
 
2.4%
Thriller12
 
2.2%
Other values (96)264
48.6%
(Missing)68
 
12.5%
2021-05-11T09:06:48.826822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fiction67
 
11.9%
contemporary30
 
5.3%
fantasy27
 
4.8%
romance27
 
4.8%
historical22
 
3.9%
humor20
 
3.6%
paranormal19
 
3.4%
memoir16
 
2.8%
mystery16
 
2.8%
adult15
 
2.7%
Other values (105)304
54.0%

Most occurring characters

ValueCountFrequency (%)
o426
 
10.4%
i390
 
9.5%
r333
 
8.1%
a305
 
7.5%
n274
 
6.7%
t249
 
6.1%
e240
 
5.9%
c185
 
4.5%
m159
 
3.9%
s159
 
3.9%
Other values (35)1368
33.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3424
83.8%
Uppercase Letter576
 
14.1%
Space Separator88
 
2.2%

Most frequent character per category

ValueCountFrequency (%)
F114
19.8%
C69
12.0%
H65
11.3%
M52
9.0%
A41
 
7.1%
P35
 
6.1%
R34
 
5.9%
B29
 
5.0%
T22
 
3.8%
S18
 
3.1%
Other values (13)97
16.8%
ValueCountFrequency (%)
o426
12.4%
i390
11.4%
r333
9.7%
a305
8.9%
n274
 
8.0%
t249
 
7.3%
e240
 
7.0%
c185
 
5.4%
m159
 
4.6%
s159
 
4.6%
Other values (11)704
20.6%
ValueCountFrequency (%)
88
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4000
97.8%
Common88
 
2.2%

Most frequent character per script

ValueCountFrequency (%)
o426
 
10.7%
i390
 
9.8%
r333
 
8.3%
a305
 
7.6%
n274
 
6.9%
t249
 
6.2%
e240
 
6.0%
c185
 
4.6%
m159
 
4.0%
s159
 
4.0%
Other values (34)1280
32.0%
ValueCountFrequency (%)
88
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4088
100.0%

Most frequent character per block

ValueCountFrequency (%)
o426
 
10.4%
i390
 
9.5%
r333
 
8.1%
a305
 
7.5%
n274
 
6.7%
t249
 
6.1%
e240
 
5.9%
c185
 
4.5%
m159
 
3.9%
s159
 
3.9%
Other values (35)1368
33.5%

genre_3
Categorical

HIGH CARDINALITY
MISSING

Distinct121
Distinct (%)25.7%
Missing72
Missing (%)13.3%
Memory size4.4 KiB
Fiction
 
25
Fantasy
 
22
Biography
 
20
Historical
 
20
Romance
 
17
Other values (116)
367 

Length

Max length24
Median length9
Mean length9.392781316
Min length3

Characters and Unicode

Total characters4424
Distinct characters47
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)10.6%

Sample

1st rowDemons
2nd rowCrime
3rd rowHigh Fantasy
4th rowLGBT
5th rowParanormal
ValueCountFrequency (%)
Fiction25
 
4.6%
Fantasy22
 
4.1%
Biography20
 
3.7%
Historical20
 
3.7%
Romance17
 
3.1%
Audiobook17
 
3.1%
Paranormal15
 
2.8%
Contemporary11
 
2.0%
Food11
 
2.0%
Crime10
 
1.8%
Other values (111)303
55.8%
(Missing)72
 
13.3%
2021-05-11T09:06:49.054028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fiction62
 
10.0%
fantasy40
 
6.5%
adult31
 
5.0%
historical25
 
4.0%
biography24
 
3.9%
romance22
 
3.6%
audiobook17
 
2.8%
science17
 
2.8%
contemporary17
 
2.8%
paranormal15
 
2.4%
Other values (119)348
56.3%

Most occurring characters

ValueCountFrequency (%)
o424
 
9.6%
i421
 
9.5%
a333
 
7.5%
r280
 
6.3%
t279
 
6.3%
e262
 
5.9%
n262
 
5.9%
c212
 
4.8%
s196
 
4.4%
l166
 
3.8%
Other values (37)1589
35.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3647
82.4%
Uppercase Letter630
 
14.2%
Space Separator147
 
3.3%

Most frequent character per category

ValueCountFrequency (%)
F124
19.7%
A74
11.7%
C63
10.0%
H56
8.9%
B40
 
6.3%
S37
 
5.9%
M35
 
5.6%
R32
 
5.1%
L25
 
4.0%
P25
 
4.0%
Other values (13)119
18.9%
ValueCountFrequency (%)
o424
11.6%
i421
11.5%
a333
9.1%
r280
 
7.7%
t279
 
7.7%
e262
 
7.2%
n262
 
7.2%
c212
 
5.8%
s196
 
5.4%
l166
 
4.6%
Other values (13)812
22.3%
ValueCountFrequency (%)
147
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4277
96.7%
Common147
 
3.3%

Most frequent character per script

ValueCountFrequency (%)
o424
 
9.9%
i421
 
9.8%
a333
 
7.8%
r280
 
6.5%
t279
 
6.5%
e262
 
6.1%
n262
 
6.1%
c212
 
5.0%
s196
 
4.6%
l166
 
3.9%
Other values (36)1442
33.7%
ValueCountFrequency (%)
147
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4424
100.0%

Most frequent character per block

ValueCountFrequency (%)
o424
 
9.6%
i421
 
9.5%
a333
 
7.5%
r280
 
6.3%
t279
 
6.3%
e262
 
5.9%
n262
 
5.9%
c212
 
4.8%
s196
 
4.4%
l166
 
3.8%
Other values (37)1589
35.9%

genre_4
Categorical

HIGH CARDINALITY
MISSING

Distinct141
Distinct (%)30.7%
Missing83
Missing (%)15.3%
Memory size4.4 KiB
Fiction
35 
Audiobook
 
31
Romance
 
18
Adult
 
17
Fantasy
 
14
Other values (136)
345 

Length

Max length28
Median length9
Mean length9.82173913
Min length3

Characters and Unicode

Total characters4518
Distinct characters48
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61 ?
Unique (%)13.3%

Sample

1st rowFantasy
2nd rowContemporary
3rd rowMagic
4th rowAdult
5th rowUrban Fantasy
ValueCountFrequency (%)
Fiction35
 
6.4%
Audiobook31
 
5.7%
Romance18
 
3.3%
Adult17
 
3.1%
Fantasy14
 
2.6%
Historical10
 
1.8%
Biography Memoir10
 
1.8%
Contemporary9
 
1.7%
Magic9
 
1.7%
Crime9
 
1.7%
Other values (131)298
54.9%
(Missing)83
 
15.3%
2021-05-11T09:06:49.282235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fiction71
 
11.5%
adult35
 
5.7%
audiobook31
 
5.0%
romance28
 
4.5%
fantasy28
 
4.5%
biography17
 
2.7%
science15
 
2.4%
memoir14
 
2.3%
historical13
 
2.1%
mystery13
 
2.1%
Other values (137)354
57.2%

Most occurring characters

ValueCountFrequency (%)
i447
 
9.9%
o431
 
9.5%
e324
 
7.2%
t306
 
6.8%
a295
 
6.5%
n276
 
6.1%
r249
 
5.5%
c228
 
5.0%
l177
 
3.9%
s166
 
3.7%
Other values (38)1619
35.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3736
82.7%
Uppercase Letter621
 
13.7%
Space Separator159
 
3.5%
Decimal Number2
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
i447
12.0%
o431
11.5%
e324
 
8.7%
t306
 
8.2%
a295
 
7.9%
n276
 
7.4%
r249
 
6.7%
c228
 
6.1%
l177
 
4.7%
s166
 
4.4%
Other values (13)837
22.4%
ValueCountFrequency (%)
F110
17.7%
A105
16.9%
S56
9.0%
M52
8.4%
C51
8.2%
R45
7.2%
H37
 
6.0%
B29
 
4.7%
P24
 
3.9%
T20
 
3.2%
Other values (12)92
14.8%
ValueCountFrequency (%)
21
50.0%
11
50.0%
ValueCountFrequency (%)
159
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4357
96.4%
Common161
 
3.6%

Most frequent character per script

ValueCountFrequency (%)
i447
 
10.3%
o431
 
9.9%
e324
 
7.4%
t306
 
7.0%
a295
 
6.8%
n276
 
6.3%
r249
 
5.7%
c228
 
5.2%
l177
 
4.1%
s166
 
3.8%
Other values (35)1458
33.5%
ValueCountFrequency (%)
159
98.8%
21
 
0.6%
11
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII4518
100.0%

Most frequent character per block

ValueCountFrequency (%)
i447
 
9.9%
o431
 
9.5%
e324
 
7.2%
t306
 
6.8%
a295
 
6.5%
n276
 
6.1%
r249
 
5.5%
c228
 
5.0%
l177
 
3.9%
s166
 
3.7%
Other values (38)1619
35.8%

genre_5
Categorical

HIGH CARDINALITY
MISSING

Distinct143
Distinct (%)31.6%
Missing90
Missing (%)16.6%
Memory size4.4 KiB
Audiobook
40 
Fiction
 
23
Adult
 
22
Urban Fantasy
 
13
Romance
 
12
Other values (138)
343 

Length

Max length24
Median length9
Mean length9.463576159
Min length2

Characters and Unicode

Total characters4287
Distinct characters51
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)15.5%

Sample

1st rowAngels
2nd rowMystery Thriller
3rd rowAdventure
4th rowComedy
5th rowVampires
ValueCountFrequency (%)
Audiobook40
 
7.4%
Fiction23
 
4.2%
Adult22
 
4.1%
Urban Fantasy13
 
2.4%
Romance12
 
2.2%
Adventure11
 
2.0%
Biography Memoir11
 
2.0%
Contemporary9
 
1.7%
Comedy9
 
1.7%
Mystery Thriller9
 
1.7%
Other values (133)294
54.1%
(Missing)90
 
16.6%
2021-05-11T09:06:49.525456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fiction45
 
7.5%
adult43
 
7.2%
audiobook40
 
6.7%
fantasy28
 
4.7%
romance20
 
3.3%
memoir17
 
2.8%
biography16
 
2.7%
urban13
 
2.2%
mystery12
 
2.0%
contemporary12
 
2.0%
Other values (137)354
59.0%

Most occurring characters

ValueCountFrequency (%)
o411
 
9.6%
i353
 
8.2%
e310
 
7.2%
a288
 
6.7%
r280
 
6.5%
t253
 
5.9%
n234
 
5.5%
l160
 
3.7%
u159
 
3.7%
c155
 
3.6%
Other values (41)1684
39.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3531
82.4%
Uppercase Letter601
 
14.0%
Space Separator147
 
3.4%
Decimal Number8
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
A116
19.3%
F98
16.3%
C54
9.0%
M46
 
7.7%
R37
 
6.2%
S34
 
5.7%
B27
 
4.5%
H27
 
4.5%
T22
 
3.7%
W22
 
3.7%
Other values (13)118
19.6%
ValueCountFrequency (%)
o411
11.6%
i353
 
10.0%
e310
 
8.8%
a288
 
8.2%
r280
 
7.9%
t253
 
7.2%
n234
 
6.6%
l160
 
4.5%
u159
 
4.5%
c155
 
4.4%
Other values (12)928
26.3%
ValueCountFrequency (%)
13
37.5%
22
25.0%
91
 
12.5%
01
 
12.5%
81
 
12.5%
ValueCountFrequency (%)
147
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4132
96.4%
Common155
 
3.6%

Most frequent character per script

ValueCountFrequency (%)
o411
 
9.9%
i353
 
8.5%
e310
 
7.5%
a288
 
7.0%
r280
 
6.8%
t253
 
6.1%
n234
 
5.7%
l160
 
3.9%
u159
 
3.8%
c155
 
3.8%
Other values (35)1529
37.0%
ValueCountFrequency (%)
147
94.8%
13
 
1.9%
22
 
1.3%
91
 
0.6%
01
 
0.6%
81
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII4287
100.0%

Most frequent character per block

ValueCountFrequency (%)
o411
 
9.6%
i353
 
8.2%
e310
 
7.2%
a288
 
6.7%
r280
 
6.5%
t253
 
5.9%
n234
 
5.5%
l160
 
3.7%
u159
 
3.7%
c155
 
3.6%
Other values (41)1684
39.3%

genre_6
Categorical

HIGH CARDINALITY
MISSING

Distinct152
Distinct (%)34.2%
Missing98
Missing (%)18.0%
Memory size4.4 KiB
Audiobook
35 
Adult
 
31
Adventure
 
17
Adult Fiction
 
16
Fiction
 
13
Other values (147)
333 

Length

Max length24
Median length9
Mean length9.617977528
Min length3

Characters and Unicode

Total characters4280
Distinct characters49
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique77 ?
Unique (%)17.3%

Sample

1st rowMythology
2nd rowAdult
3rd rowScience Fiction Fantasy
4th rowRelationships
5th rowAngels
ValueCountFrequency (%)
Audiobook35
 
6.4%
Adult31
 
5.7%
Adventure17
 
3.1%
Adult Fiction16
 
2.9%
Fiction13
 
2.4%
Magic13
 
2.4%
Fantasy12
 
2.2%
Biography Memoir10
 
1.8%
Romance9
 
1.7%
Childrens9
 
1.7%
Other values (142)280
51.6%
(Missing)98
 
18.0%
2021-05-11T09:06:49.769677image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
adult61
 
10.2%
fiction47
 
7.9%
audiobook35
 
5.9%
fantasy31
 
5.2%
adventure17
 
2.8%
biography15
 
2.5%
romance15
 
2.5%
magic13
 
2.2%
memoir12
 
2.0%
science12
 
2.0%
Other values (145)339
56.8%

Most occurring characters

ValueCountFrequency (%)
i382
 
8.9%
o380
 
8.9%
e302
 
7.1%
t283
 
6.6%
a250
 
5.8%
n244
 
5.7%
r226
 
5.3%
l191
 
4.5%
u177
 
4.1%
c171
 
4.0%
Other values (39)1674
39.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3530
82.5%
Uppercase Letter598
 
14.0%
Space Separator152
 
3.6%

Most frequent character per category

ValueCountFrequency (%)
A137
22.9%
F88
14.7%
C53
 
8.9%
M45
 
7.5%
S40
 
6.7%
R29
 
4.8%
B28
 
4.7%
P27
 
4.5%
H27
 
4.5%
T19
 
3.2%
Other values (15)105
17.6%
ValueCountFrequency (%)
i382
10.8%
o380
10.8%
e302
 
8.6%
t283
 
8.0%
a250
 
7.1%
n244
 
6.9%
r226
 
6.4%
l191
 
5.4%
u177
 
5.0%
c171
 
4.8%
Other values (13)924
26.2%
ValueCountFrequency (%)
152
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4128
96.4%
Common152
 
3.6%

Most frequent character per script

ValueCountFrequency (%)
i382
 
9.3%
o380
 
9.2%
e302
 
7.3%
t283
 
6.9%
a250
 
6.1%
n244
 
5.9%
r226
 
5.5%
l191
 
4.6%
u177
 
4.3%
c171
 
4.1%
Other values (38)1522
36.9%
ValueCountFrequency (%)
152
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4280
100.0%

Most frequent character per block

ValueCountFrequency (%)
i382
 
8.9%
o380
 
8.9%
e302
 
7.1%
t283
 
6.6%
a250
 
5.8%
n244
 
5.7%
r226
 
5.3%
l191
 
4.5%
u177
 
4.1%
c171
 
4.0%
Other values (39)1674
39.1%

genre_7
Categorical

HIGH CARDINALITY
MISSING

Distinct152
Distinct (%)34.4%
Missing101
Missing (%)18.6%
Memory size4.4 KiB
Audiobook
 
34
Adult
 
27
Fiction
 
16
Urban Fantasy
 
12
Adult Fiction
 
11
Other values (147)
342 

Length

Max length27
Median length9
Mean length9.450226244
Min length2

Characters and Unicode

Total characters4177
Distinct characters52
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)16.7%

Sample

1st rowAdult
2nd rowYoung Adult
3rd rowRomance
4th rowHoliday
5th rowFiction
ValueCountFrequency (%)
Audiobook34
 
6.3%
Adult27
 
5.0%
Fiction16
 
2.9%
Urban Fantasy12
 
2.2%
Adult Fiction11
 
2.0%
Romance11
 
2.0%
Supernatural10
 
1.8%
Fantasy9
 
1.7%
Science Fiction Fantasy9
 
1.7%
Magic9
 
1.7%
Other values (142)294
54.1%
(Missing)101
 
18.6%
2021-05-11T09:06:50.007893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fiction47
 
8.1%
adult45
 
7.8%
fantasy36
 
6.2%
audiobook34
 
5.9%
romance22
 
3.8%
contemporary14
 
2.4%
science13
 
2.2%
urban12
 
2.1%
novels11
 
1.9%
book10
 
1.7%
Other values (145)334
57.8%

Most occurring characters

ValueCountFrequency (%)
o388
 
9.3%
i348
 
8.3%
e302
 
7.2%
t283
 
6.8%
a260
 
6.2%
n258
 
6.2%
r237
 
5.7%
u177
 
4.2%
c176
 
4.2%
s166
 
4.0%
Other values (42)1582
37.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3454
82.7%
Uppercase Letter581
 
13.9%
Space Separator136
 
3.3%
Decimal Number6
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
A122
21.0%
F94
16.2%
C58
10.0%
S47
 
8.1%
R31
 
5.3%
H28
 
4.8%
M28
 
4.8%
B26
 
4.5%
T22
 
3.8%
N17
 
2.9%
Other values (13)108
18.6%
ValueCountFrequency (%)
o388
11.2%
i348
10.1%
e302
 
8.7%
t283
 
8.2%
a260
 
7.5%
n258
 
7.5%
r237
 
6.9%
u177
 
5.1%
c176
 
5.1%
s166
 
4.8%
Other values (13)859
24.9%
ValueCountFrequency (%)
12
33.3%
21
16.7%
01
16.7%
91
16.7%
61
16.7%
ValueCountFrequency (%)
136
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4035
96.6%
Common142
 
3.4%

Most frequent character per script

ValueCountFrequency (%)
o388
 
9.6%
i348
 
8.6%
e302
 
7.5%
t283
 
7.0%
a260
 
6.4%
n258
 
6.4%
r237
 
5.9%
u177
 
4.4%
c176
 
4.4%
s166
 
4.1%
Other values (36)1440
35.7%
ValueCountFrequency (%)
136
95.8%
12
 
1.4%
21
 
0.7%
01
 
0.7%
91
 
0.7%
61
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII4177
100.0%

Most frequent character per block

ValueCountFrequency (%)
o388
 
9.3%
i348
 
8.3%
e302
 
7.2%
t283
 
6.8%
a260
 
6.2%
n258
 
6.2%
r237
 
5.7%
u177
 
4.2%
c176
 
4.2%
s166
 
4.0%
Other values (42)1582
37.9%

genre_8
Categorical

HIGH CARDINALITY
MISSING

Distinct155
Distinct (%)35.6%
Missing108
Missing (%)19.9%
Memory size4.4 KiB
Adult
40 
Audiobook
33 
Adult Fiction
 
13
Science Fiction Fantasy
 
10
Fiction
 
10
Other values (150)
329 

Length

Max length28
Median length9
Mean length9.848275862
Min length3

Characters and Unicode

Total characters4284
Distinct characters51
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79 ?
Unique (%)18.2%

Sample

1st rowUrban Fantasy
2nd rowThriller
3rd rowAdult
4th rowEssays
5th rowSupernatural
ValueCountFrequency (%)
Adult40
 
7.4%
Audiobook33
 
6.1%
Adult Fiction13
 
2.4%
Science Fiction Fantasy10
 
1.8%
Fiction10
 
1.8%
Supernatural8
 
1.5%
Adventure8
 
1.5%
Urban Fantasy8
 
1.5%
Biography Memoir8
 
1.5%
Paranormal Romance7
 
1.3%
Other values (145)290
53.4%
(Missing)108
 
19.9%
2021-05-11T09:06:50.256119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
adult64
 
10.6%
fiction53
 
8.8%
audiobook33
 
5.5%
fantasy29
 
4.8%
romance19
 
3.1%
science15
 
2.5%
biography14
 
2.3%
young11
 
1.8%
mystery11
 
1.8%
historical10
 
1.7%
Other values (151)345
57.1%

Most occurring characters

ValueCountFrequency (%)
i388
 
9.1%
o375
 
8.8%
t302
 
7.0%
a285
 
6.7%
e274
 
6.4%
n261
 
6.1%
r244
 
5.7%
u186
 
4.3%
l185
 
4.3%
c178
 
4.2%
Other values (41)1606
37.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3508
81.9%
Uppercase Letter603
 
14.1%
Space Separator169
 
3.9%
Decimal Number4
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
A142
23.5%
F93
15.4%
C45
 
7.5%
S39
 
6.5%
M34
 
5.6%
R30
 
5.0%
B28
 
4.6%
H27
 
4.5%
T20
 
3.3%
P20
 
3.3%
Other values (14)125
20.7%
ValueCountFrequency (%)
i388
11.1%
o375
10.7%
t302
 
8.6%
a285
 
8.1%
e274
 
7.8%
n261
 
7.4%
r244
 
7.0%
u186
 
5.3%
l185
 
5.3%
c178
 
5.1%
Other values (14)830
23.7%
ValueCountFrequency (%)
13
75.0%
21
 
25.0%
ValueCountFrequency (%)
169
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4111
96.0%
Common173
 
4.0%

Most frequent character per script

ValueCountFrequency (%)
i388
 
9.4%
o375
 
9.1%
t302
 
7.3%
a285
 
6.9%
e274
 
6.7%
n261
 
6.3%
r244
 
5.9%
u186
 
4.5%
l185
 
4.5%
c178
 
4.3%
Other values (38)1433
34.9%
ValueCountFrequency (%)
169
97.7%
13
 
1.7%
21
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII4284
100.0%

Most frequent character per block

ValueCountFrequency (%)
i388
 
9.1%
o375
 
8.8%
t302
 
7.0%
a285
 
6.7%
e274
 
6.4%
n261
 
6.1%
r244
 
5.7%
u186
 
4.3%
l185
 
4.3%
c178
 
4.2%
Other values (41)1606
37.5%

genre_9
Categorical

HIGH CARDINALITY
MISSING

Distinct149
Distinct (%)34.7%
Missing113
Missing (%)20.8%
Memory size4.4 KiB
Adult
46 
Audiobook
32 
Fiction
 
15
Novels
 
14
Supernatural
 
12
Other values (144)
311 

Length

Max length28
Median length9
Mean length9.274418605
Min length3

Characters and Unicode

Total characters3988
Distinct characters49
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)17.2%

Sample

1st rowSupernatural
2nd rowAdult Fiction
3rd rowAudiobook
4th rowBiography
5th rowMagic
ValueCountFrequency (%)
Adult46
 
8.5%
Audiobook32
 
5.9%
Fiction15
 
2.8%
Novels14
 
2.6%
Supernatural12
 
2.2%
Family10
 
1.8%
Literary Fiction9
 
1.7%
Magic9
 
1.7%
Adult Fiction9
 
1.7%
Adventure7
 
1.3%
Other values (139)267
49.2%
(Missing)113
20.8%
2021-05-11T09:06:50.479321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
adult63
 
11.1%
fiction48
 
8.4%
audiobook32
 
5.6%
fantasy21
 
3.7%
novels18
 
3.2%
supernatural12
 
2.1%
romance11
 
1.9%
science10
 
1.8%
family10
 
1.8%
mystery10
 
1.8%
Other values (140)334
58.7%

Most occurring characters

ValueCountFrequency (%)
o360
 
9.0%
i335
 
8.4%
t279
 
7.0%
e271
 
6.8%
a242
 
6.1%
r219
 
5.5%
n209
 
5.2%
l202
 
5.1%
u190
 
4.8%
c160
 
4.0%
Other values (39)1521
38.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3280
82.2%
Uppercase Letter565
 
14.2%
Space Separator139
 
3.5%
Decimal Number4
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
A128
22.7%
F91
16.1%
C51
 
9.0%
S47
 
8.3%
M33
 
5.8%
N23
 
4.1%
H23
 
4.1%
B20
 
3.5%
L19
 
3.4%
R18
 
3.2%
Other values (13)112
19.8%
ValueCountFrequency (%)
o360
11.0%
i335
10.2%
t279
 
8.5%
e271
 
8.3%
a242
 
7.4%
r219
 
6.7%
n209
 
6.4%
l202
 
6.2%
u190
 
5.8%
c160
 
4.9%
Other values (12)813
24.8%
ValueCountFrequency (%)
12
50.0%
91
25.0%
21
25.0%
ValueCountFrequency (%)
139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3845
96.4%
Common143
 
3.6%

Most frequent character per script

ValueCountFrequency (%)
o360
 
9.4%
i335
 
8.7%
t279
 
7.3%
e271
 
7.0%
a242
 
6.3%
r219
 
5.7%
n209
 
5.4%
l202
 
5.3%
u190
 
4.9%
c160
 
4.2%
Other values (35)1378
35.8%
ValueCountFrequency (%)
139
97.2%
12
 
1.4%
91
 
0.7%
21
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII3988
100.0%

Most frequent character per block

ValueCountFrequency (%)
o360
 
9.0%
i335
 
8.4%
t279
 
7.0%
e271
 
6.8%
a242
 
6.1%
r219
 
5.5%
n209
 
5.2%
l202
 
5.1%
u190
 
4.8%
c160
 
4.0%
Other values (39)1521
38.1%

genre_0_weight
Real number (ℝ≥0)

MISSING

Distinct65
Distinct (%)13.2%
Missing51
Missing (%)9.4%
Infinite0
Infinite (%)0.0%
Mean0.4283333333
Minimum0.17
Maximum1
Zeros0
Zeros (%)0.0%
Memory size4.4 KiB
2021-05-11T09:06:50.584417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.17
5-th percentile0.23
Q10.31
median0.39
Q30.5025
95-th percentile0.76
Maximum1
Range0.83
Interquartile range (IQR)0.1925

Descriptive statistics

Standard deviation0.1671434457
Coefficient of variation (CV)0.3902181611
Kurtosis2.425568335
Mean0.4283333333
Median Absolute Deviation (MAD)0.09
Skewness1.421413009
Sum210.74
Variance0.02793693143
MonotocityNot monotonic
2021-05-11T09:06:50.690513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3325
 
4.6%
0.3918
 
3.3%
0.3816
 
2.9%
0.316
 
2.9%
0.3115
 
2.8%
0.5215
 
2.8%
0.3615
 
2.8%
0.4414
 
2.6%
0.3514
 
2.6%
0.414
 
2.6%
Other values (55)330
60.8%
(Missing)51
 
9.4%
ValueCountFrequency (%)
0.172
 
0.4%
0.194
0.7%
0.24
0.7%
0.215
0.9%
0.224
0.7%
ValueCountFrequency (%)
112
2.2%
0.961
 
0.2%
0.951
 
0.2%
0.931
 
0.2%
0.911
 
0.2%

genre_1_weight
Real number (ℝ≥0)

MISSING

Distinct41
Distinct (%)8.5%
Missing63
Missing (%)11.6%
Infinite0
Infinite (%)0.0%
Mean0.2010833333
Minimum0.01
Maximum0.5
Zeros0
Zeros (%)0.0%
Memory size4.4 KiB
2021-05-11T09:06:50.795608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.0995
Q10.15
median0.2
Q30.24
95-th percentile0.32
Maximum0.5
Range0.49
Interquartile range (IQR)0.09

Descriptive statistics

Standard deviation0.07087047002
Coefficient of variation (CV)0.3524432823
Kurtosis1.64647828
Mean0.2010833333
Median Absolute Deviation (MAD)0.04
Skewness0.4859372165
Sum96.52
Variance0.005022623521
MonotocityNot monotonic
2021-05-11T09:06:50.903706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0.2135
 
6.4%
0.1831
 
5.7%
0.1731
 
5.7%
0.2329
 
5.3%
0.2429
 
5.3%
0.229
 
5.3%
0.1426
 
4.8%
0.2225
 
4.6%
0.2724
 
4.4%
0.1923
 
4.2%
Other values (31)198
36.5%
(Missing)63
 
11.6%
ValueCountFrequency (%)
0.011
 
0.2%
0.022
0.4%
0.033
0.6%
0.041
 
0.2%
0.051
 
0.2%
ValueCountFrequency (%)
0.53
0.6%
0.41
 
0.2%
0.392
0.4%
0.381
 
0.2%
0.372
0.4%

genre_2_weight
Real number (ℝ≥0)

MISSING

Distinct27
Distinct (%)5.7%
Missing68
Missing (%)12.5%
Infinite0
Infinite (%)0.0%
Mean0.1215789474
Minimum0.01
Maximum0.29
Zeros0
Zeros (%)0.0%
Memory size4.4 KiB
2021-05-11T09:06:51.002796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.05
Q10.09
median0.12
Q30.15
95-th percentile0.2
Maximum0.29
Range0.28
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.04759879332
Coefficient of variation (CV)0.3915052264
Kurtosis0.04858597814
Mean0.1215789474
Median Absolute Deviation (MAD)0.03
Skewness0.3820716657
Sum57.75
Variance0.002265645125
MonotocityNot monotonic
2021-05-11T09:06:51.090876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.1149
 
9.0%
0.140
 
7.4%
0.1239
 
7.2%
0.0836
 
6.6%
0.0934
 
6.3%
0.1532
 
5.9%
0.1630
 
5.5%
0.1428
 
5.2%
0.0728
 
5.2%
0.1327
 
5.0%
Other values (17)132
24.3%
(Missing)68
12.5%
ValueCountFrequency (%)
0.012
 
0.4%
0.025
 
0.9%
0.034
 
0.7%
0.044
 
0.7%
0.0517
3.1%
ValueCountFrequency (%)
0.291
 
0.2%
0.271
 
0.2%
0.253
0.6%
0.241
 
0.2%
0.236
1.1%

genre_3_weight
Real number (ℝ≥0)

MISSING

Distinct18
Distinct (%)3.8%
Missing72
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean0.07893842887
Minimum0.01
Maximum0.18
Zeros0
Zeros (%)0.0%
Memory size4.4 KiB
2021-05-11T09:06:51.181959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.03
Q10.06
median0.08
Q30.1
95-th percentile0.14
Maximum0.18
Range0.17
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation0.03233696306
Coefficient of variation (CV)0.4096479182
Kurtosis0.02030850159
Mean0.07893842887
Median Absolute Deviation (MAD)0.02
Skewness0.4624131809
Sum37.18
Variance0.00104567918
MonotocityNot monotonic
2021-05-11T09:06:51.260550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.0762
11.4%
0.0862
11.4%
0.0551
9.4%
0.0650
9.2%
0.0944
8.1%
0.143
7.9%
0.0433
6.1%
0.1125
 
4.6%
0.1223
 
4.2%
0.0322
 
4.1%
Other values (8)56
10.3%
(Missing)72
13.3%
ValueCountFrequency (%)
0.015
 
0.9%
0.026
 
1.1%
0.0322
4.1%
0.0433
6.1%
0.0551
9.4%
ValueCountFrequency (%)
0.182
 
0.4%
0.174
 
0.7%
0.162
 
0.4%
0.155
 
0.9%
0.1414
2.6%

genre_4_weight
Real number (ℝ≥0)

MISSING

Distinct14
Distinct (%)3.0%
Missing83
Missing (%)15.3%
Infinite0
Infinite (%)0.0%
Mean0.05645652174
Minimum0.01
Maximum0.14
Zeros0
Zeros (%)0.0%
Memory size4.4 KiB
2021-05-11T09:06:51.345627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.02
Q10.04
median0.06
Q30.07
95-th percentile0.0905
Maximum0.14
Range0.13
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.02244936837
Coefficient of variation (CV)0.3976399481
Kurtosis0.2289895907
Mean0.05645652174
Median Absolute Deviation (MAD)0.02
Skewness0.4329449534
Sum25.97
Variance0.0005039741404
MonotocityNot monotonic
2021-05-11T09:06:51.436710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.0681
14.9%
0.0479
14.5%
0.0573
13.4%
0.0766
12.2%
0.0339
7.2%
0.0838
7.0%
0.0229
 
5.3%
0.0926
 
4.8%
0.113
 
2.4%
0.016
 
1.1%
Other values (4)10
 
1.8%
(Missing)83
15.3%
ValueCountFrequency (%)
0.016
 
1.1%
0.0229
 
5.3%
0.0339
7.2%
0.0479
14.5%
0.0573
13.4%
ValueCountFrequency (%)
0.141
 
0.2%
0.131
 
0.2%
0.124
 
0.7%
0.114
 
0.7%
0.113
2.4%

genre_5_weight
Real number (ℝ≥0)

MISSING

Distinct10
Distinct (%)2.2%
Missing90
Missing (%)16.6%
Infinite0
Infinite (%)0.0%
Mean0.04269315673
Minimum0.01
Maximum0.1
Zeros0
Zeros (%)0.0%
Memory size4.4 KiB
2021-05-11T09:06:51.530795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.02
Q10.03
median0.04
Q30.05
95-th percentile0.07
Maximum0.1
Range0.09
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.01745502113
Coefficient of variation (CV)0.40884822
Kurtosis-0.2637365076
Mean0.04269315673
Median Absolute Deviation (MAD)0.01
Skewness0.331979872
Sum19.34
Variance0.0003046777628
MonotocityNot monotonic
2021-05-11T09:06:51.603861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.0394
17.3%
0.0492
16.9%
0.0585
15.7%
0.0660
11.0%
0.0255
10.1%
0.0733
 
6.1%
0.0117
 
3.1%
0.0814
 
2.6%
0.12
 
0.4%
0.091
 
0.2%
(Missing)90
16.6%
ValueCountFrequency (%)
0.0117
 
3.1%
0.0255
10.1%
0.0394
17.3%
0.0492
16.9%
0.0585
15.7%
ValueCountFrequency (%)
0.12
 
0.4%
0.091
 
0.2%
0.0814
 
2.6%
0.0733
6.1%
0.0660
11.0%

genre_6_weight
Real number (ℝ≥0)

MISSING

Distinct9
Distinct (%)2.0%
Missing98
Missing (%)18.0%
Infinite0
Infinite (%)0.0%
Mean0.03314606742
Minimum0.01
Maximum0.1
Zeros0
Zeros (%)0.0%
Memory size4.4 KiB
2021-05-11T09:06:51.680931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q10.02
median0.03
Q30.04
95-th percentile0.06
Maximum0.1
Range0.09
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.01378694918
Coefficient of variation (CV)0.4159452464
Kurtosis0.9109033508
Mean0.03314606742
Median Absolute Deviation (MAD)0.01
Skewness0.5711696851
Sum14.75
Variance0.0001900799676
MonotocityNot monotonic
2021-05-11T09:06:51.755999image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.03134
24.7%
0.04105
19.3%
0.0289
16.4%
0.0553
 
9.8%
0.0137
 
6.8%
0.0620
 
3.7%
0.075
 
0.9%
0.11
 
0.2%
0.081
 
0.2%
(Missing)98
18.0%
ValueCountFrequency (%)
0.0137
 
6.8%
0.0289
16.4%
0.03134
24.7%
0.04105
19.3%
0.0553
 
9.8%
ValueCountFrequency (%)
0.11
 
0.2%
0.081
 
0.2%
0.075
 
0.9%
0.0620
 
3.7%
0.0553
9.8%

genre_7_weight
Real number (ℝ≥0)

MISSING

Distinct8
Distinct (%)1.8%
Missing101
Missing (%)18.6%
Infinite0
Infinite (%)0.0%
Mean0.02714932127
Minimum0
Maximum0.07
Zeros2
Zeros (%)0.4%
Memory size4.4 KiB
2021-05-11T09:06:51.835071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.02
median0.03
Q30.03
95-th percentile0.05
Maximum0.07
Range0.07
Interquartile range (IQR)0.01

Descriptive statistics

Standard deviation0.01152815196
Coefficient of variation (CV)0.4246202637
Kurtosis0.5935828272
Mean0.02714932127
Median Absolute Deviation (MAD)0.01
Skewness0.510652917
Sum12
Variance0.0001328982875
MonotocityNot monotonic
2021-05-11T09:06:51.911140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.03148
27.3%
0.02132
24.3%
0.0473
13.4%
0.0161
11.2%
0.0520
 
3.7%
0.063
 
0.6%
0.073
 
0.6%
02
 
0.4%
(Missing)101
18.6%
ValueCountFrequency (%)
02
 
0.4%
0.0161
11.2%
0.02132
24.3%
0.03148
27.3%
0.0473
13.4%
ValueCountFrequency (%)
0.073
 
0.6%
0.063
 
0.6%
0.0520
 
3.7%
0.0473
13.4%
0.03148
27.3%

genre_8_weight
Real number (ℝ≥0)

MISSING

Distinct7
Distinct (%)1.6%
Missing108
Missing (%)19.9%
Infinite0
Infinite (%)0.0%
Mean0.02271264368
Minimum0
Maximum0.06
Zeros5
Zeros (%)0.9%
Memory size4.4 KiB
2021-05-11T09:06:51.996217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.02
median0.02
Q30.03
95-th percentile0.04
Maximum0.06
Range0.06
Interquartile range (IQR)0.01

Descriptive statistics

Standard deviation0.01022771608
Coefficient of variation (CV)0.4503093617
Kurtosis0.8370827633
Mean0.02271264368
Median Absolute Deviation (MAD)0.01
Skewness0.6704163621
Sum9.88
Variance0.0001046061762
MonotocityNot monotonic
2021-05-11T09:06:52.070284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.02179
33.0%
0.03117
21.5%
0.0191
16.8%
0.0430
 
5.5%
0.0510
 
1.8%
05
 
0.9%
0.063
 
0.6%
(Missing)108
19.9%
ValueCountFrequency (%)
05
 
0.9%
0.0191
16.8%
0.02179
33.0%
0.03117
21.5%
0.0430
 
5.5%
ValueCountFrequency (%)
0.063
 
0.6%
0.0510
 
1.8%
0.0430
 
5.5%
0.03117
21.5%
0.02179
33.0%

genre_9_weight
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)1.4%
Missing113
Missing (%)20.8%
Infinite0
Infinite (%)0.0%
Mean0.01904651163
Minimum0
Maximum0.05
Zeros9
Zeros (%)1.7%
Memory size4.4 KiB
2021-05-11T09:06:52.150357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.01
median0.02
Q30.02
95-th percentile0.03
Maximum0.05
Range0.05
Interquartile range (IQR)0.01

Descriptive statistics

Standard deviation0.008678278455
Coefficient of variation (CV)0.4556361094
Kurtosis0.1652352427
Mean0.01904651163
Median Absolute Deviation (MAD)0.01
Skewness0.4435659793
Sum8.19
Variance7.531251694 × 105
MonotocityNot monotonic
2021-05-11T09:06:52.232431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.02185
34.1%
0.01138
25.4%
0.0383
15.3%
0.0413
 
2.4%
09
 
1.7%
0.052
 
0.4%
(Missing)113
20.8%
ValueCountFrequency (%)
09
 
1.7%
0.01138
25.4%
0.02185
34.1%
0.0383
15.3%
0.0413
 
2.4%
ValueCountFrequency (%)
0.052
 
0.4%
0.0413
 
2.4%
0.0383
15.3%
0.02185
34.1%
0.01138
25.4%

price
Real number (ℝ≥0)

Distinct377
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean231296.7624
Minimum0
Maximum978395
Zeros1
Zeros (%)0.2%
Memory size4.4 KiB
2021-05-11T09:06:52.332522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile72041
Q1128922.5
median212946
Q3287224
95-th percentile529982.6
Maximum978395
Range978395
Interquartile range (IQR)158301.5

Descriptive statistics

Standard deviation138233.5085
Coefficient of variation (CV)0.5976456698
Kurtosis2.937813855
Mean231296.7624
Median Absolute Deviation (MAD)83012
Skewness1.401843799
Sum125594142
Variance1.910850287 × 1010
MonotocityNot monotonic
2021-05-11T09:06:52.445625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14422611
 
2.0%
7204111
 
2.0%
23084810
 
1.8%
142939
 
1.7%
2164119
 
1.7%
1153528
 
1.5%
1297896
 
1.1%
1222816
 
1.1%
431676
 
1.1%
864785
 
0.9%
Other values (367)462
85.1%
ValueCountFrequency (%)
01
 
0.2%
142939
1.7%
287302
 
0.4%
425891
 
0.2%
431676
1.1%
ValueCountFrequency (%)
9783951
0.2%
8081831
0.2%
7485581
0.2%
7258921
0.2%
6928321
0.2%

Interactions

2021-05-11T09:05:56.061870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:56.168967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:56.277066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:56.382160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:56.497265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:56.610368image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:56.727474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:56.832569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:56.937665image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:57.044762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:57.148856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:57.253951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:57.365052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:57.474151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:57.592258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:57.692349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:57.794442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:57.904542image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:58.010638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:58.115733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:58.221830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:58.323922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:58.421010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:58.515095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:58.616187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:58.718280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:58.826378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:58.919462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:59.011546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:59.112638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:59.205722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:59.302810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:59.402901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:59.501991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:59.599079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:59.690161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:59.781244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:59.880334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:05:59.976421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:00.071507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:00.169596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:00.274692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:00.372781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:00.471870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:00.576966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:00.679058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:00.782152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:00.878239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:00.972324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:01.070414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:01.164499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:01.259585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:01.359676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:01.459767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:01.559858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:01.652943image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:01.747027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:01.848119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:01.947209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:02.043297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:02.147391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:02.248482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:02.341567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:02.436653image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:02.536744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:02.636339image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:02.740433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:02.832517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:02.923599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:03.017685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:03.108767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:03.201852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:03.307948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:03.414044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:03.520140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:03.624234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:03.723325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:03.830421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:03.934516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:04.035608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:04.131695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:04.239793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:04.340885image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:04.444484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:04.545575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:04.652673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:04.764774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:04.863864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:04.962954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:05.066047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:05.164136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:05.265228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:05.368322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:05.474418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:05.577512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:05.677603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:05.776692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:05.883789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:05.987884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:06.089976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:06.193070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:06.300167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:06.399257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:06.502351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:06.603443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:06.709538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:06.819638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:07.810537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:07.910628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:08.012721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:08.110810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:08.209899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:08.312993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:08.416086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:08.519180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:08.616268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:08.719362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:08.824457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:08.926549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:09.025640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:09.127732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:09.240835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:09.344929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:09.448023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:09.554119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:09.666221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:09.777321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:09.878413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:09.977503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:10.080597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:10.184691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:10.289786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:10.398885image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:10.508985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:10.619085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:10.721178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:10.823270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:10.934371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:11.041468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:11.146563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:11.255663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:11.353752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:11.446836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:11.541922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:11.633006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:11.731094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:11.828182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:11.927272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:12.017354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:12.110438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:12.199519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:12.290601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:12.385688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:12.482775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:12.577863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:12.667944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:12.756528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:12.853616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:12.946701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:13.037783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:13.290012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:13.387100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:13.479184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:13.575271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:13.665352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:13.764442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:13.861530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:13.959619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:14.049701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:14.142786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:14.230865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:14.321948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:14.416555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:14.511642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:14.605727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:14.692806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:14.780886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-05-11T09:06:35.091338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:35.191429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:35.291519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:35.391610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:35.484694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:35.580782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:35.680873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:35.781964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:35.881054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:35.985149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:36.076231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:36.168315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:36.264401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:36.364492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:36.463583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:36.554665image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:36.657759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:36.755848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:36.850934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:36.950024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:37.051115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:37.151206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:37.249295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:37.356392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:37.454481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:37.561579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:37.661669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:37.766765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:38.168129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:38.279229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:38.382323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:38.486417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:38.591513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:38.686599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:38.784688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:38.885779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:38.986871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:39.091967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:39.186052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:39.280138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:39.383231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-11T09:06:39.483322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-05-11T09:06:52.561730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-11T09:06:52.814472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-11T09:06:53.066702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-11T09:06:53.329940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-05-11T09:06:53.601187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-05-11T09:06:39.745559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-11T09:06:41.205388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-05-11T09:06:41.769900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-05-11T09:06:43.213730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexauthor_iddescriptionbookformatbookeditionpagespublished_datepublisher_idreading_agelexile_measuregrade_levelweightrating_value_0rating_value_1rating_count_0rating_count_1dimension_0dimension_1dimension_2genre_0genre_1genre_2genre_3genre_4genre_5genre_6genre_7genre_8genre_9genre_0_weightgenre_1_weightgenre_2_weightgenre_3_weightgenre_4_weightgenre_5_weightgenre_6_weightgenre_7_weightgenre_8_weightgenre_9_weightprice
09author0932At long last, New York Times bestselling author Gena Showalter unveils the story of Paris, the darkest and most tormented Lord of the Underworld.Possessed by the demon of Promiscuity, immortal warrior Paris is irresistibly seductive — but his potent allure comes at a terrible price. Every night he must bed someone new, or weaken and die. And the woman he craves above all others is the one woman he'd thought was forever beyond his reach... until now.Newly possessed by the demon of Wrath, Sienna Blackstone is racked by a ruthless need to punish those around her. Yet in Paris's arms, the vulnerable beauty finds soul-searing passion and incredible peace. Until a blood feud between ancient enemies heats up.Will the battle against gods, angels and creatures of the night bind them eternally — or tear them apart?Mass Market PaperbackNaN504.0February 28, 2012publisher149NaNNaNNaN3628.744.294.62698350410.723.3016.79Paranormal RomanceParanormalRomanceDemonsFantasyAngelsMythologyAdultUrban FantasySupernatural0.260.210.170.100.080.050.040.040.030.0298172.0
117author2279I sold my future to the man who ruined my past.I had a plan:Sign a contract and board a plane to Ibiza. The anonymous deal would salvage the smoldering wreckage of my life.It would not involve billionaire Harrison King. AKA, the reason I need saving in the first place. He’s as beautiful as he is cruel. A British business titan who’s practically royalty and makes a living getting what he wants.The man flies private. Dates supermodels. But the crisp accent and cocky smirk don’t fool me. He’s a gentleman on the outside, a savage beneath. Dangerous, rough and brutal.Because after my attempt to publicly stand up for those who needed it... He destroyed my reputation. Now, he’s come for the rest of me.Except it’s not salvation he’s promising. It’s nights in Ibiza, under his roof, rules, his control. I sold my soul to a man I hate. Now, he owns me.I can’t back out. No matter what kind of punishment he has in store. Harrison King knows my secrets...But kings keep secrets too.BEAUTIFUL ENEMY is an enthralling, explosive romance from USA Today bestselling author Piper Lawson! You’ll want to binge this addictive new story.Kindle EditionNaNNaNNaNNaNNaNNaNNaNNaN4.89NaN271NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN57604.0
229author2386From Rob Thomas, the creator of groundbreaking television series and movie Veronica Mars, comes the first book in a thrilling new mystery series.Ten years after graduating from high school in Neptune, California, Veronica Mars is back in the land of sun, sand, crime, and corruption. She's traded in her law degree for her old private investigating license, struggling to keep Mars Investigations afloat on the scant cash earned by catching cheating spouses until she can score her first big case.Now it's spring break, and college students descend on Neptune, transforming the beaches and boardwalks into a frenzied, week-long rave. When a girl disappears from a party, Veronica is called in to investigate. But this is not a simple missing person's case. The house the girl vanished from belongs to a man with serious criminal ties, and soon Veronica is plunged into a dangerous underworld of drugs and organized crime. And when a major break in the investigation has a shocking connection to Veronica's past, the case hits closer to home than she ever imagined.PaperbackNaN324.0March 25, 2014publisher381NaNNaNNaN3991.613.994.643657153713.311.8520.22MysteryFictionAudiobookCrimeContemporaryMystery ThrillerAdultYoung AdultThrillerAdult Fiction0.450.190.070.070.060.050.040.040.030.02103658.0
333author2769Hannis Arc, working on the tapestry of lines linking constellations of elements that constituted the language of Creation recorded on the ancient Cerulean scroll spread out among the clutter on his desk, was not surprised to see the seven etherial forms billow into the room like acrid smoke driven on a breath of bitter breeze. Like an otherworldly collection of spectral shapes seemingly carried on random eddies of air, they wandered in a loose clutch among the still and silent mounted bears and beasts rising up on their stands, the small forest of stone pedestals holding massive books of recorded prophecy, and the evenly spaced display cases of oddities, their glass reflecting the firelight from the massive hearth at the side of the room.Since the seven rarely used doors, the shutters on the windows down on the ground level several stories below stood open as a fearless show of invitation. Though they frequently chose to use windows, they didn’t actually need the windows any more than they needed the doors. They could seep through any opening, any crack, like vapor rising in the early morning from the stretches of stagnant water that lay in dark swaths through the peat barrens.The open shutters were meant to be a declaration for all to see, including the seven, that Hannis Arc feared nothing.#1 New York Times-bestselling author Terry Goodkind returns to the lives of Richard Rahl and Kahlan Amnell—in a compelling tale of a new and sinister threat to their world.In addition to concluding the Sword of Truth series, The Omen Machine also launches the new series of "Richard and Kahlan."HardcoverNaN528.0August 16, 2011publisher099NaNNaNNaN771.113.684.319382150416.514.3224.43FantasyFictionEpic FantasyHigh FantasyMagicAdventureScience Fiction FantasyRomanceAdultAudiobook0.770.070.050.020.020.020.020.010.010.01649665.0
444author0473C.A. Nicholas's magnum opus symphony is about to begin and he's reserved a seat for you. So come on in and I'll lead you to your place of honour as the house lights dim. Yes, your spot is beside the maestro as he teleports you and he through various worlds to befriend diverse souls who'll reveal the beauty of your life through their stories.***C.A. Nicholas's Interlaced Souls Series has ended and he has collected it into one tome on your behalf. "Cycles of the Phoenix" consists of "Sanity's War", "Strange: And Other Accounts from the Taboo War", and "Kaya: Where Have You Gone?"Winner of 𝑳𝒊𝒕𝒑𝒊𝒄𝒌'𝒔 𝑻𝒐𝒑 𝑪𝒉𝒐𝒊𝒄𝒆 𝑩𝒐𝒐𝒌 𝑨𝒘𝒂𝒓𝒅.Winner of 𝑳𝒊𝒕𝒆𝒓𝒂𝒓𝒚 𝑻𝒊𝒕𝒂𝒏'𝒔 𝑺𝒊𝒍𝒗𝒆𝒓 𝑨𝒘𝒂𝒓𝒅."As subsequent stories drastically recontextualize former ones, the multi-genre 𝘊𝘺𝘤𝘭𝘦𝘴 𝘰𝘧 𝘵𝘩𝘦 𝘗𝘩𝘰𝘦𝘯𝘪𝘹 universe ultimately reveals itself to be an urgently life-affirming, humanizing, and self-empathic tragedy, as well as a bittersweet tale."- 𝑻𝒉𝒆 𝑨𝒕𝒍𝒂𝒏𝒕𝒊𝒔 𝑷𝒐𝒔𝒕"Within one thematically brutal and adult oriented tale that's mostly seen through kids' perceptions, there's an original children's story which allegorizes most of the 'real life' narrative."- 𝑻𝒉𝒆 𝑳𝒂𝒌𝒆 𝑷𝒂𝒓𝒌 𝑻𝒊𝒎𝒆𝒔"This book shows that C.A. Nicholas isn't a poetic storyteller. Rather, he is a poet who creates stories; his tales have the bodies of narratives whose souls are poems, cascading with symbolism, rhythm, dreamlike (including nightmarish) sensory descriptions, events which rhyme with one another, and conventionless form."- 𝑻𝒉𝒆 𝑫𝒓𝒚 𝑻𝒐𝒓𝒕𝒐𝒈𝒖𝒔 𝑪𝒐𝒖𝒓𝒓𝒊𝒆𝒓"Though there is some slasher level brutality here, the overwhelming majority of the violence is psychologically graphic within this empathic book."- 𝑻𝒉𝒆 𝑷𝒂𝒉𝒐𝒐𝒌𝒆𝒆 𝑷𝒐𝒔𝒕"The author's style seems to be inspired by C.S. Lewis, Edgar Allen Poe... and Vincent Van Gough."- 𝑻𝒉𝒆 𝑷𝒂𝒔𝒔𝒊𝒐𝒏𝒂𝒕𝒆 𝑷𝒂𝒊𝒏𝒕𝒆𝒓𝒔' 𝑱𝒐𝒖𝒓𝒏𝒂𝒍"𝘊𝘺𝘤𝘭𝘦𝘴 𝘰𝘧 𝘵𝘩𝘦 𝘗𝘩𝘰𝘦𝘯𝘪𝘹 dares to find uplifting messages not only at the peaks of optimism and humour, but in the deepest trenches of raw trauma, grief, and despair."- 𝑻𝒉𝒆 𝑻𝒘𝒆𝒏𝒕𝒚-𝑭𝒐𝒖𝒓 𝑯𝒐𝒖𝒓 𝑵𝒊𝒈𝒉𝒕𝒔 𝑫𝒊𝒔𝒑𝒂𝒕𝒄𝒉"There are plot driven stories and character driven ones yet even the former variety of tales are rooted in the characters' psychology."- 𝑻𝒉𝒆 𝑪𝒓𝒆𝒂𝒕𝒊𝒗𝒊𝒕𝒚 𝑾𝒊𝒕𝒉𝒊𝒏 𝑨𝒍𝒍 𝒐𝒇 𝑼𝒔 𝑶𝒓𝒈𝒂𝒏𝒊𝒛𝒂𝒕𝒊𝒐𝒏PaperbackNaN500.0May 5, 2019publisher184NaNNaNNaN725.753.813.632915.243.1722.86NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN247883.0
549author2883How come the only thing my family tree ever grows is nuts?” Wade Rouse attempts to answer that question in his blisteringly funny new memoir by looking at the yearly celebrations that unite us all and bring out the very best and worst in our nearest and dearest. Family is truly the only gift that keeps on giving—namely, the gifts of dysfunction and eccentricity— and Wade Rouse’s family has been especially charitable: His chatty yet loving mother dresses her son as a Ubangi tribesman, in blackface, for Halloween in the rural Ozarks; his unconventional engineer of a father buries his children’s Easter eggs; his marvelously Martha Stewart–esque partner believes Barbie is his baby; his garage-sale obsessed set of in-laws are convinced they can earn more than Warren Buffett by selling their broken lamps and Nehru jackets; his mutt Marge speaks her own language; and his oddball collection of relatives includes a tipsy Santa Claus with an affinity for showing off his jingle balls. In the end, though, the Rouse House gifted Wade with love, laughter, understanding, superb comic timing, and a humbling appreciation for humiliation. Whether Wade dates a mime on his birthday to overcome his phobia of clowns or outruns a chub-chasing boss on Secretary’s Day, he captures our holidays with his trademark self-deprecating humor and acerbic wit. He paints a funny, sad, poignant, and outlandish portrait of an an all-too-typical family that will have you appreciating—or bemoaning—your own and shrieking in laughter.HardcoverNaN304.0February 1, 2011publisher074NaNNaNNaN544.313.804.314156016.082.7724.13MemoirNonfictionHumorLGBTAdultComedyRelationshipsHolidayEssaysBiography0.350.250.220.040.030.030.030.030.030.02117806.0
650author0441The New York Times bestselling Mortal Instruments continues—and so do the thrills and danger for Jace, Clary, and Simon.What price is too high to pay, even for love? When Jace and Clary meet again, Clary is horrified to discover that the demon Lilith’s magic has bound her beloved Jace together with her evil brother Sebastian, and that Jace has become a servant of evil. The Clave is out to destroy Sebastian, but there is no way to harm one boy without destroying the other. As Alec, Magnus, Simon, and Isabelle wheedle and bargain with Seelies, demons, and the merciless Iron Sisters to try to save Jace, Clary plays a dangerous game of her own. The price of losing is not just her own life, but Jace’s soul. She’s willing to do anything for Jace, but can she still trust him? Or is he truly lost?Love. Blood. Betrayal. Revenge. Darkness threatens to claim the Shadowhunters in the harrowing fifth book of the Mortal Instruments series.HardcoverNaN535.0May 8, 2012publisher22414 years and upHL740L9 - 12680.394.254.7454617553315.243.0522.86FantasyYoung AdultRomanceParanormalUrban FantasyVampiresAngelsFictionSupernaturalMagic0.340.240.090.090.060.050.040.040.030.03122281.0
756author2507The year is 2059. Nineteen-year-old Paige Mahoney is working in the criminal underworld of Scion London, based at Seven Dials, employed by a man named Jaxon Hall. Her job: to scout for information by breaking into people’s minds. For Paige is a dreamwalker, a clairvoyant and, in the world of Scion, she commits treason simply by breathing.It is raining the day her life changes for ever. Attacked, drugged and kidnapped, Paige is transported to Oxford – a city kept secret for two hundred years, controlled by a powerful, otherworldly race. Paige is assigned to Warden, a Rephaite with mysterious motives. He is her master. Her trainer. Her natural enemy. But if Paige wants to regain her freedom she must allow herself to be nurtured in this prison where she is meant to die.The Bone Season introduces a compelling heroine and also introduces an extraordinary young writer, with huge ambition and a teeming imagination. Samantha Shannon has created a bold new reality in this riveting debut.HardcoverUS466.0August 20, 2013publisher04514 years and upNaNNaN771.113.774.165792162316.814.0923.95FantasyYoung AdultDystopiaFictionScience FictionParanormalUrban FantasyRomanceAdultAudiobook0.440.140.090.080.070.060.040.030.030.02122281.0
859author0401The team at King’s Row must face the school that defeated them in the fencing state championships last year, but first Nicholas and Seiji must learn to work together as a team...and maybe something more!FOILED AGAIN?Just as Nicholas, Seiji and the fencing team at the prodigious Kings Row private school seem to be coming together, a deadly rival from their past stands in their way once more. MacRobertson is the school that knocked Kings Row out of the State Championships last year - but unless Nicholas and Seiji can learn to work together as a team, their school is doomed once again! And maybe those two can learn to be something more than teammates too…PaperbackNaN112.0NaNNaNNaNNaNNaNNaN4.324.84401266NaNNaNNaNGraphic NovelsComicsYoung AdultLGBTContemporarySportsQueerRomanceFictionGraphic Novels Comics0.320.130.120.120.110.080.040.030.030.03189847.0
980author0501After decades of hiding, the evil Enchantress who cursed Sleeping Beauty is back with a vengeance.Alex and Conner Bailey have not been back to the magical Land of Stories since their adventures in The Wishing Spell ended. But one night, they learn the famed Enchantress has kidnapped their mother! Against the will of their grandmother, the twins must find their own way into the Land of Stories to rescue their mother and save the fairy tale world from the greatest threat it's ever faced.HardcoverNaN517.0August 6, 2013publisher2138 - 12 years760L3 - 7566.994.484.844321254714.614.4520.32FantasyMiddle GradeFictionChildrensYoung AdultFairy TalesAdventureMagicAudiobookRetellings0.440.150.060.060.060.060.050.040.030.03225939.0

Last rows

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5333494author2754With the opening line of Silver Sparrow, “My father, James Witherspoon is a bigamist,” Tayari Jones unveils a breathtaking story about a man’s deception, a family’s complicity, and the teenage girls caught in the middle.Set in a middle-class neighborhood in Atlanta in the 1980s, the novel revolves around James Witherspoon’s families– the public one and the secret one. When the daughters from each family meet and form a friendship, only one of them knows they are sisters. It is a relationship destined to explode when secrets are revealed and illusions shattered. As Jones explores the backstories of her rich and flawed characters, she also reveals the joy, and the destruction, they brought to each other’s lives.At the heart of it all are the two girls whose lives are at stake, and like the best writers, Jones portrays the fragility of her characers with raw authenticity as they seek love, demand attention, and try to imagine themselves as women.HardcoverNaN352.0May 24, 2011publisher008NaN770LNaN476.273.924.428096317914.762.8721.74FictionHistorical FictionAfrican AmericanAudiobookLiterary FictionAdultContemporaryAdult FictionComing Of AgeNovels0.520.120.070.060.050.040.030.030.030.03389799.0
5343495author1062The New York Times bestselling Culture novel. . .The Scavenger species are circling. It is, truly, provably, the End Days for the Gzilt civilization.An ancient people, organized on military principles and yet almost perversely peaceful, the Gzilt helped set up the Culture ten thousand years earlier and were very nearly one of its founding societies, deciding not to join only at the last moment. Now they've made the collective decision to follow the well-trodden path of millions of other civilizations; they are going to Sublime, elevating themselves to a new and almost infinitely more rich and complex existence.Amid preparations though, the Regimental High Command is destroyed. Lieutenant Commander (reserve) Vyr Cossont appears to have been involved, and she is now wanted -- dead, not alive. Aided only by an ancient, reconditioned android and a suspicious Culture avatar, Cossont must complete her last mission given to her by the High Command. She must find the oldest person in the Culture, a man over nine thousand years old, who might have some idea what really happened all that time ago.It seems that the final days of the Gzilt civilization are likely to prove its most perilous.HardcoverNaN528.0October 9, 2012publisher261NaNNaNNaN793.794.174.520794124916.514.4524.13Science FictionFictionSpace OperaCulturalScience Fiction FantasyAudiobookSpeculative FictionSpaceBanksNovels0.600.160.110.040.030.020.010.010.010.01130655.0
5353496author2672In the predawn hours, in a distressed American city, hundreds of unemployed men and women line up for the opening of a job fair. They are tired and cold and desperate. Emerging from the fog, invisible until it is too late, a lone driver plows through the crowd in a stolen Mercedes, running over the innocent, backing up, and charging again. Eight people are killed; fifteen are wounded. The killer escapes.Months later, an ex-cop named bill Hodges, still haunted by the unsolved crime, contemplates suicide. When he gets a crazed letter from "the perk," claiming credit for the murders, Hodges wakes up from his depressed and vacant retirement, fearing another even more diabolical attack and hell-bent on preventing it.Brady Hartfield lives with his alcoholic mother in the house where he was born. He loved the feel of death under the wheels of the Mercedes, and he wants that rush again. Only Bill Hodges, with a couple of eccentric and mismatched allies, can apprehend the killer before he strikes again. And they have no time to lose, because Brady's next mission, if it succeeds, will kill or maim thousands.Mr. Mercedes is a war between good and evil from the master of suspense whose insight into the mind of this obsessed, insane killer is chilling and unforgettable.--front flapHardcover1st Scribner hardcover edition (US/CAN)437.0June 3, 2014publisher318NaNNaNNaN793.793.964.52605311414415.573.8123.50FictionThrillerHorrorMysteryCrimeAudiobookMystery ThrillerSuspenseAdultDetective0.210.200.180.150.090.050.040.040.020.02254380.0
5363506author0663Originally released as an online serial where itreceivedmore than 70,000 downloads,John Dies at the End has been described as a"Horrortacular", an epic of "spectacular" horror that combines the laugh out loud humor of the best R-rated comedy, with the darkest terror of H.P. Lovecraft. The book went on to sell an additional60,000 copies in all formats.As thesequel opens, we find our heroes, David and John, again embroiled in a series of horrifying yet mind-bogglingly ridiculous events caused primarily by their own gross incompetence. The guys find that books and movies about zombies may have triggered a zombie apocalypse, despite a complete lack of zombies in the world. As they race against the clock to protect humanity from its own paranoia, they must ask themselves, who are the real monsters? Actually, that would be the shape-shifting horrors secretly taking over the world behind the scenes that, in the end, make John and Dave kind of wish it had been zombies after all.Hilarious, terrifying, engaging and wrenching, This Book Is Full of Spiders, the next thrilling installment, takes us for a wild ride with two slackers from the midwest who really have better things to do with their time than prevent the apocalypse.HardcoverNaN406.0October 2, 2012publisher360NaNNaNNaN793.794.244.826123194215.242.6922.86HorrorFictionHumorFantasyScience FictionComedyZombiesUrban FantasyAudiobookParanormal0.400.170.120.100.070.050.030.020.020.02577336.0
5373514author1229In this YA contemporary queer romance from the author of \n\nHot Dog Girl\n\n, an openly gay track star falls for a closeted, bisexual teen beauty queen with a penchant for fixing up old cars.\nMorgan, an elite track athlete, is forced to transfer high schools late in her senior year after it turns out being queer is against her private Catholic school's code of conduct. There, she meets Ruby, who has two hobbies: tinkering with her baby blue 1970 Ford Torino and competing in local beauty pageants, the latter to live out the dreams of her overbearing mother. The two are drawn to each other and can't deny their growing feelings. But while Morgan--out and proud, and determined to have a fresh start--doesn't want to have to keep their budding relationship a secret, Ruby isn't ready to come out yet. With each girl on a different path toward living her truth, can they go the distance together?HardcoverNaN336.0May 18, 2021publisher12912 - 17 yearsNaN7 - 9566.994.15NaN61114.612.8221.74LGBTContemporaryRomanceYoung AdultLesbianQueerFictionYoung Adult ContemporarySportsGay0.240.200.150.140.110.070.030.030.020.01230848.0
5383518author1012Harlan Coben, the master of domestic suspense, returns with a standalone thriller in the vein of #1 bestsellers Hold Tight, Caught and Stay Close that explores the depth and passion of a lost love . . . and the secrets and lies at its heart. Six years have passed since Jake Fisher watched Natalie, the love of his life, marry another man. Six years of hiding a broken heart by throwing himself into his career as a college professor. Six years of keeping his promise to leave Natalie alone, and six years of tortured dreams of her life with her new husband, Todd. But six years haven’t come close to extinguishing his feelings, and when Jake comes across Todd’s obituary, he can’t keep himself away from the funeral. There he gets the glimpse of Todd’s wife he’s hoping for . . . but she is not Natalie. Whoever the mourning widow is, she’s been married to Todd for more than a decade, and with that fact everything Jake thought he knew about the best time of his life—a time he has never gotten over—is turned completely inside out. As Jake searches for the truth, his picture-perfect memories of Natalie begin to unravel. Mutual friends of the couple either can’t be found or don’t remember Jake. No one has seen Natalie in years. Jake’s search for the woman who broke his heart—and who lied to him—soon puts his very life at risk as it dawns on him that the man he has become may be based on carefully constructed fiction. Harlan Coben once again delivers a shocking page-turner that deftly explores the power of past love and the secrets and lies that such love can hide.HardcoverNaN351.0March 19, 2013publisher105NaNNaNNaN512.563.834.271693594315.883.8123.52MysteryThrillerFictionMystery ThrillerSuspenseCrimeAudiobookAdultRomanceDrama0.280.200.170.090.080.070.030.030.020.02262176.0
5393522author0083Alexander McCall Smith’s beloved, bestselling No. 1 Ladies’ Detective Agency series continues as Botswana’s best and kindest detective finds her personal and professional lives have become entangled. Precious Ramotswe is very busy these days. The best apprentice at Tlokweng Road Speedy Motors is in trouble with the law and stuck with the worst lawyer in Gaborone. Grace Makutsi and Phuti Radiphuti are building the house of their dreams, but their builder is not completely on the up and up. Most shockingly, Mma Potokwane, the orphan farm’s respected matron, has been dismissed from her post. Mma Ramotswe is not about to rest when her friends are mistreated. Help arrives from an unexpected visitor. He is none other than the estimable Mr. Clovis Andersen, author of The Principles of Private Detection, the No. 1 Ladies’ prized manual. Together, Mma Ramotswe, Mma Makutsi, and their colleague help right injustices that occur even in their beloved Botswana, and in the process discover something new about being a good detective.HardcoverNaN257.0April 3, 2012publisher267NaNNaNNaN566.994.124.714157140716.232.6224.21MysteryFictionAfricaBotswanaCrimeHumorCozy MysteryDetectiveAudiobookAdult0.370.250.160.060.040.030.030.030.020.02216411.0
5403529author0975From one of the most acclaimed and profound writers in the world of comics comes a thrilling and provocative exploration of humankind’s great modern myth: the superhero The first superhero comic ever published, Action Comics no. 1 in 1938, introduced the world to something both unprecedented and timeless: Superman, a caped god for the modern age. In a matter of years, the skies of the imaginary world were filled with strange mutants, aliens, and vigilantes: Batman, Wonder Woman, the Fantastic Four, Iron Man, and the X-Men—the list of names as familiar as our own. In less than a century, they’ve gone from not existing at all to being everywhere we look: on our movie and television screens, in our videogames and dreams. But what are they trying to tell us?For Grant Morrison, arguably the greatest of contemporary chroniclers of the “superworld,” these heroes are powerful archetypes whose ongoing, decades-spanning story arcs reflect and predict the course of human existence: Through them we tell the story of ourselves, our troubled history, and our starry aspirations. In this exhilarating work of a lifetime, Morrison draws on art, science, mythology, and his own astonishing journeys through this shadow universe to provide the first true history of the superhero—why they matter, why they will always be with us, and what they tell us about who we are . . . and what we may yet become.HardcoverNaN444.0July 19, 2011publisher341NaNNaNNaN780.183.844.4775824016.263.2024.16NonfictionComicsHistoryBiographySuperheroesGraphic NovelsPop CultureMemoirPhilosophyCultural0.430.230.070.070.060.050.030.030.030.02152310.0
5413538author0034Go the Fuck to Sleep is a bedtime book for parents who live in the real world, where a few snoozing kitties and cutesy rhymes don't always send a toddler sailing blissfully off to dreamland. Profane, affectionate, and radically honest, California Book Award-winning author Adam Mansbach's verses perfectly capture the familiar -- and unspoken -- tribulations of putting your little angel down for the night. In the process, he opens up a conversation about parenting, granting us permission to admit our frustrations and laugh at their absurdities.With illustrations by Ricardo Cortes, Go the Fuck to Sleep is beautiful, subversive and pants-wettingly funny, a book for parents new, old and expectant. You probably should not read it to your children.HardcoverNaN64.0June 14, 2011publisher005NaNNaNNaN3265.864.264.8857671550421.341.0215.75HumorFictionPicture BooksChildrensComedyAudiobookParentingPoetryAdultShort Stories0.390.140.090.080.070.060.060.050.040.02176853.0
5423541author1187"Unexplained Fevers plucks the familiar fairy tale heroines and drops them into alternate landscapes. Unlocking them from the old stories is a way to 'rescue the other half of [their] souls.' And so Sleeping Beauty arrives at the emergency room, Red Riding Hood reaches the car dealership, and Rapunzel goes wandering in the desert - their journeys, re-imagined in this inventive collection of poems, produce other dangers, betrayals and nightmares, but also bring forth great surprise and wonder." —Rigoberto González, author of Black Blossoms Unexplained Fevers, the third full-length poetry collection from Jeannine Hall Gailey, is due for release in Spring 2013 from New Binary Press.PaperbackNaN76.0March 30, 2013publisher253NaNNaNNaN1741.794.244.8851114.810.4621.01PoetryFantasyFairy TalesFictionRetellingsNaNNaNNaNNaNNaN0.710.100.080.060.05NaNNaNNaNNaNNaN216555.0